Colon Cancer Disease Diagnosis Based on Convolutional Neural Network and Fishier Mantis Optimizer

Citation: Mohamed, A.A.A.; Hançerlio˘gullari, A.; Rahebi, J.; Rezaeizadeh, R.; Lopez-Guede, J.M. Colon Cancer Disease Diagnosis Based on Convolutional Neural Network and Fishier Mantis Optimizer. Diagnostics 2024,14, 1417. https://doi.org/10.3390/ diagnostics14131417 Received: 28 May 2024 Revised: 25 June 2024 Accepted: 27 June 2024 Published: 2 July 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). diagnostics Article Colon Cancer Disease Diagnosis Based on Convolutional Neural Network and Fishier Mantis Optimizer Amna Ali A. Mohamed 1, Aybaba Hançerlio˘gullari 2, Javad Rahebi 3,* , Rezvan Rezaeizadeh 4 and Jose Manuel Lopez-Guede 5,* 1Department of Material Science and Engineering, University of Kastamonu, Kastamonu 37150, Turkey; [email protected] 2Department of Physics, University of Kastamonu, Kastamonu 37150, Turkey; [email protected] 3Department of Software Engineering, Istanbul Topkapi University, Istanbul 34087, Turkey 4Department of Physics, Faculty of Science, University of Guilan, Rasht P.O. Box 41335-1914, Guilan, Iran; r[email protected] 5Department of Systems and Automatic Control, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), C/Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain *Correspondence: [email protected] (J.R.); [email protected] (J.M.L.-G.) Abstract: Colon cancer is a prevalent and potentially fatal disease that demands early and accurate diagnosis for effective treatment. Traditional diagnostic approaches for colon cancer often face limitations in accuracy and efficiency, leading to challenges in early detection and treatment. In response to these challenges, this paper introduces an innovative method that leverages artificial intelligence, specifically convolutional neural network (CNN) and Fishier Mantis Optimizer, for the automated detection of colon cancer. The utilization of deep learning techniques, specifically CNN, enables the extraction of intricate features from medical imaging data, providing a robust and efficient diagnostic model. Additionally, the Fishier Mantis Optimizer, a bio-inspired optimization algorithm inspired by the hunting behavior of the mantis shrimp, is employed to fine-tune the parameters of the CNN, enhancing its convergence speed and performance. This hybrid approach aims to address the limitations of traditional diagnostic methods by leveraging the strengths of both deep learning and nature-inspired optimization to enhance the accuracy and effectiveness of colon cancer diagnosis. The proposed method was evaluated on a comprehensive dataset comprising colon cancer images, and the results demonstrate its superiority over traditional diagnostic approaches. The CNN–Fishier Mantis Optimizer model exhibited high sensitivity, specificity, and overall accuracy in distinguishing between cancer and non-cancer colon tissues. The integration of bio-inspired optimization algorithms with deep learning techniques not only contributes to the advancement of computer-aided diagnostic tools for colon cancer but also holds promise for enhancing the early detection and diagnosis of this disease, thereby facilitating timely intervention and improved patient prognosis. Various CNN designs, such as GoogLeNet and ResNet-50, were employed to capture features associated with colon diseases. However, inaccuracies were introduced in both feature extraction and data classification due to the abundance of features. To address this issue, feature reduction techniques were implemented using Fishier Mantis Optimizer algorithms, outperforming alternative methods such as Genetic Algorithms and simulated annealing. Encouraging results were obtained in the evaluation of diverse metrics, including sensitivity, specificity, accuracy, and F1-Score, which were found to be 94.87%, 96.19%, 97.65%, and 96.76%, respectively. Keywords: convolutional neural network; metaheuristic methods; FMO; Fishier Mantis Optimizer; colon cancer 1. Introduction In recent years, colon cancer has emerged as a significant cause of mortality, affecting millions of individuals worldwide [ 1 – 3 ]. Lifestyle factors, aging, and genetics are known Diagnostics 2024,14, 1417. https://doi.org/10.3390/diagnostics14131417 https://www.mdpi.com/journal/diagnostics Diagnostics 2024,14, 1417 2 of 16 to contribute to the development of colon cancer, with research establishing a clear link between the consumption of processed meats and alcohol and an increased risk of developing the disease [ 4 , 5 ]. Moreover, studies indicate a higher prevalence of this disease in developed countries, with approximately 65% of cases diagnosed in these regions [6]. However, traditional approaches to colon cancer diagnosis present significant challenges in terms of early identification and accurate diagnosis, making it a prevalent and life-threatening disease [ 5 ]. However, traditional approaches to colon cancer diagnosis present significant challenges in terms of early identification and accurate diagnosis, making it a prevalent and life-threatening disease [ 1 – 3 ]. To address these challenges and enhance the accuracy and effectiveness of colon cancer diagnosis, advanced technologies such as machine learning and deep learning have been explored in medical image analysis [ 7 – 9 ]. Artificial intelligence offers solutions to the limitations of traditional approaches by utilizing techniques such as artificial neural networks (ANNs), support vector machine (SVM), fuzzy methods, expert systems, and metaheuristic methods for disease diagnosis using medical images [10–17]. Additionally, innovative methods for diagnosing and categorizing colon cancer histopathological images are essential to improve the precision of diagnosis and ultimately improve patient outcomes. This revised introduction provides a clearer and more structured overview of the challenges in colon cancer diagnosis and the role of artificial intelligence in addressing these challenges. It eliminates repetitions and organizes the information in a more focused and coherent manner. In general, progress in medical image analysis shows potential for transforming early detection and diagnosis of colon cancer, ultimately leading to improved outcomes for patients. Researchers are striving to optimize the diagnostic process and enhance the overall management of this life-threatening illness by leveraging state-of-the-art technologies and innovative algorithms. Given the significant global burden of colon cancer, innovative approaches are continuously sought to enhance diagnostic accuracy and treatment efficacy. This research addresses this need by focusing on the following key areas: • introduction of an innovative method for categorizing colon cancer histopathological images without feature selection using PCA; • utilization of an intelligent feature selection method with FMO algorithm to enhance the precision of colon cancer diagnosis; • integration of AI, deep learning, and bio-inspired optimization algorithms for improved early detection and diagnosis of colon cancer; • focus on streamlining the detection process, improving diagnostic accuracy, and ultimately enhancing patient outcomes; • potential revolutionization of cancer diagnosis and treatment through cutting-edge technologies in medical imaging analysis. The study organization provides a brief outline of the content in each section. Section 2 reviews the literature, Section 3describes the materials and methods, Sections 4and 5 presents the results and discussion, and Section 6provides the conclusion. This overview helps readers understand the structure of the study and anticipate the content of each section. 2. Literature Review In recent years, colon cancer has claimed numerous lives, making it a significant concern worldwide [ 18 ]. Preventive measures, including a healthy lifestyle and regular screening, are essential for reducing the risk of colon cancer [ 19 ]. Diagnostic imaging plays a crucial role in identifying various diseases, including Alzheimer’s disease, Multiple Sclerosis, and colorectal carcinoma [ 20 ]. With its life-threatening nature, colon cancer requires early detection and accurate diagnosis for effective treatment [21]. Medical imaging modalities such as CT scans and MRI techniques aid in diagnosing colon cancer by detecting abnormal cell growth in the colon [ 22 ]. Lifestyle factors, aging, and genetics contribute to the development of colon cancer, with processed meats and Diagnostics 2024,14, 1417 3 of 16 alcohol consumption being associated with increased risk [ 23 ]. Screening methods like colonoscopy and histopathological screenings are vital for early detection and prevention [ 24 ]. Medical imaging, coupled with advancements in machine learning and deep learning, shows promise in improving the accuracy of colon cancer diagnosis [25]. Table 1summarizes various research studies aimed at improving the diagnosis and prognosis of colorectal cancer through innovative methodologies such as automated algorithms, convolutional neural networks (CNNs), and integration of medical data with artificial intelligence (AI) techniques. Each study outlines its aims, advantages, disadvantages, and results, showcasing diverse approaches to addressing the challenges in colorectal cancer diagnosis and treatment. Table 1. Advancements in colorectal cancer diagnosis and prognosis: a comparative study of innovative approaches. Aims Advantages Disadvantages Results Ref 1. Develop an automated algorithm for detecting and categorizing hyperplastic and adenomatous colorectal polyps during colonoscopy. 2. Utilize transfer learning from large nonmedical datasets to enhance the precision of polyp detection and classification. 3. Investigate the capability of the proposed method to aid endoscopists in promptly resecting adenomatous polyps. 1. The algorithm streamlines the process of identifying and categorizing colorectal polyps, thereby reducing the time and expenses associated with manual examination. 2. Demonstrating high precision, recall rate, and accuracy compared to visual inspection by endoscopists, the algorithm potentially mitigates the risk of overlooking adenomatous polyps. 3. Drawing insights from nonmedical datasets enriches the algorithm’s performance, underscoring the efficacy of transfer learning in analyzing medical images. 4. By precisely identifying adenomatous polyps, the algorithm facilitates prompt resection before they progress into invasive cancer, which could enhance patient outcomes. 1. Despite the effectiveness of transfer learning, there may be a disconnect between nonmedical source tasks and medical target tasks, necessitating further exploration to optimize feature selection. 2. Gathering and annotating a substantial volume of medical data to fine-tune CNN architecture may pose challenges, given the limited availability and complexity of medical datasets. 3. The proposed method may demand sophisticated computational resources and expertise in deep learning, potentially limiting its applicability across all healthcare settings. The proposed algorithm demonstrated comparable precision but superior recall rate and accuracy compared to visual inspection by endoscopists. Outperforming prior state-of-the-art methods with minimal preprocessing, the algorithm proved effective in assisting endoscopists in identifying overlooked adenomatous polyps. These encouraging outcomes suggest that the proposed method holds promise for enhancing the early detection and diagnosis of colorectal cancer, ultimately leading to improved patient outcomes. [ 26 ] 1. Develop a Dual-Path Convolutional Neural Network (DP-CNN) to automatically detect intestinal polyps from colonoscopy images. 2. Validate the effectiveness of the proposed DP-CNN model in detecting polyps through experimental results. 3. Evaluate the performance of the proposed method in terms of recall, precision, F1-Score, F2-Score, and overall accuracy across different databases. 1. The proposed DP-CNN model exhibits high recall, precision, F1-Score, and F2-Score in identifying polyps in both the CVC ColonDB and ETIS-Larib databases. 2. The proposed method offers lower complexity and fewer learnable parameters compared to existing deep learning models, making it suitable for real-time applications and scenarios with limited computing resources. 3. The DP-CNN architecture, coupled with a sigmoid classifier, effectively identifies polyps from colonoscopy images, facilitating early diagnosis and intervention. 1. The study focuses specifically on detecting polyps from colonoscopy images and may not be directly transferable to other medical image analysis tasks. 2. The performance of the proposed method is contingent on the quality and diversity of the training datasets, potentially restricting its applicability to different patient demographics or imaging conditions. 3. Further optimization and fine-tuning of hyperparameters may be necessary to enhance the robustness and generalizability of the proposed method across various datasets and clinical environments. The DP-CNN model achieves high accuracy in detecting polyps, with recall rates of 99.20% and 92.85%, precision rates of 100% and 89.81%, F1-Scores of 99.60% and 91.00%, and F2-Scores of 99.83% and 89.91% on the CVC ColonDB and ETIS-Larib databases, respectively. Comparative analysis reveals superior performance compared to existing methods, demonstrating its potential for automating polyp detection and enhancing early colorectal cancer diagnosis. [ 27 ] 1. To innovate ensembles integrating convolutional neural networks (CNNs) and transformers for semantic segmentation. 2. To validate the efficacy of merging diverse models, training methodologies, and optimization techniques to forge more potent ensembles. 3. To propose a novel approach for acquiring segmentation masks via intermediate prediction averaging. 4. To extend findings to diverse application domains and investigate strategies for adapting the model to resource-constrained hardware. 1. Ensembles combining CNNs and transformers exhibit superior polyp segmentation compared to alternative methods. 2. Intermediate prediction averaging diminishes overfitting, bolstering the model’s resilience. 3. The proposed methodology displays potential across multiple domains beyond polyp segmentation. 4. Ongoing research will explore distillation methods and pruning techniques to tailor the model for low-cost hardware. 1. Ensembles may necessitate substantial computational resources for both training and inference. 2. Performance could fluctuate based on dataset characteristics used for training and evaluation. 3. Ensembles pose challenges in discerning the contributions of individual models to the final segmentation outcome. The devised ensembles excel across five major polyp segmentation datasets, notably outperforming leading methods on two datasets without specific fine-tuning. A novel strategy of averaging intermediate predictions significantly contributes to mitigating overfitting and refining model contributions, underscoring its pivotal role in the ensembles’ success [ 28 ] 1. Create MEDomics, a dynamic infrastructure for organizing diverse health data and ensuring data quality. 2. Utilize artificial intelligence to predict individual prognosis in oncology using MEDomics. 3. Validate the effectiveness of the MEDomics framework in oncology by identifying correlations between clinical factors and mortality. 4. Utilize natural language processing (NLP) to continuously update prognosis estimates as disease conditions evolve. 1. MEDomics systematically organizes health data and continuously evaluates data quality. 2. The framework identifies clinically significant associations, such as the strong link between the Framingham risk score and cancer mortality. 3. Discoveries like the Framingham risk score’s impact on mortality can guide clinical decisions, potentially enhancing patient outcomes. 4. NLP enables ongoing adjustments to prognosis estimates, enabling personalized and timely interventions. 1. Many hospitals may lack readiness to integrate data science into clinical workflows, hindering the widespread adoption of systems like MEDomics. 2. Ensuring data accuracy and reliability within MEDomics requires ongoing attention and resource allocation. 3. The complexity of AI algorithms and NLP techniques may impede understanding among clinicians and healthcare providers. 4. Developing and sustaining dynamic infrastructures like MEDomics entails substantial investments in personnel, technology, and infrastructure. MEDomics proves its efficacy in oncology by revealing the strong association between the Framingham risk score and cancer mortality across different stages. Integration of NLP facilitates continual prognosis updates, adapting to evolving disease conditions. This framework offers a promising avenue for leveraging AI and diverse health data to enhance individual prognosis and guide clinical decision-making in oncology. [ 29 ] 1. Demonstrate the viability of integrating serum Raman spectroscopy with a convolutional neural network (CNN) model for the diagnosis of four cancer types: gastric, colon, rectal, and lung cancer. 2. Assess the accuracy of this integrated approach and visualize the CNN-extracted features specifically for rectal cancer diagnosis. 3. Explore the potential of using serum Raman spectroscopy and CNN to differentiate between cancer and healthy individuals, with a particular focus on rectal cancer. 1. The amalgamation of serum Raman spectroscopy and CNN achieved a notable classification accuracy of 94.5%, showcasing its effectiveness in diagnosing diverse cancer types. 2. The visualization of CNN-extracted features aids in deciphering chemical compositions, offering potential insights into cancer development mechanisms. 3. Serum Raman spectroscopy presents a cost-effective, rapid, and non-destructive method for cancer screening, potentially facilitating prompt detection and intervention. 1. The opaque nature of CNN models impedes transparency in understanding the learning process, potentially limiting interpretability in the diagnostic process. 2. Despite the high accuracy observed, the precise mechanisms underlying biochemical substances in different cancer types remain incompletely understood, necessitating further research for clarification. 3. While the study focuses on diagnosing four specific cancer types, the applicability of the approach to other cancer types may require additional validation and optimization efforts. The integration of serum Raman spectroscopy with a CNN model achieved a notable 94.5% accuracy in diagnosing multiple cancer types. Visualization of CNN features highlighted significant differences between cancer and healthy samples, indicating potential for non-invasive cancer screening and warranting further research into its mechanisms and applicability. [ 30 ] Diagnostics 2024,14, 1417 4 of 16 Table 1. Cont. Aims Advantages Disadvantages Results Ref 1. Develop an automated system employing convolutional neural network (CNN) and Ranking algorithm for colorectal cancer detection, aiming to alleviate pathologists’ workload and enhance diagnostic accuracy. 2. Assess the feasibility and efficacy of utilizing deep learning techniques in tissue-based diagnostics, utilizing openly available digital pathology datasets. 3. Investigate the potential of integrating CNN and Long Short-Term Memory (LSTM) to optimize performance and efficacy in colorectal cancer diagnosis and potentially extend applicability to diverse cancer types. 1. The proposed model exhibits superior prediction accuracy compared to existing methods, potentially facilitating early detection and prevention of colon cancer. 2. Serving as a screening tool, the automated system has the potential to reduce pathologists’ workload and minimize diagnostic subjectivity. 3. Incorporating CNN and LSTM enhances performance and expedites diagnosis, thereby improving system efficiency. 1. Manual feature selection from datasets may be laborious and could potentially impede system efficiency, necessitating meticulous consideration and potential optimization. 2. The model’s focus on detecting colorectal cancer may constrain its utility to other cancer types, warranting further research to broaden its scope. 3. The model’s performance may be influenced by the quality and diversity of input datasets, potentially limiting its generalizability across various populations or imaging conditions. The proposed model, employing CNN and Ranking algorithm, demonstrates superior performance in colorectal cancer diagnosis compared to existing methods, as indicated by higher Recall, Precision, and Accuracy metrics. Integration of CNN and LSTM enhances the model’s efficiency and opens avenues for potential expansion to identify various cancer types, promising advancements in medical image diagnosis frameworks. [ 31 ] 1. Explore the spatial distribution of T cell subsets in the tumor microenvironment among colon cancer patients. 2. Establish connections between spatial T cell distribution and previously analyzed genomic data in the AC-ICAM colon cancer patient group. 3. Investigate the potential prognostic significance of T cell spatial distribution concerning patient survival and Immunologic Constant of Rejection (ICR) transcriptome correlation. 1. Provides valuable insights into the intricate interplay between immune cells and the development of colon cancer. 2. Integrates spatial T cell distribution data with genomic insights, enhancing comprehension of tumor-immune dynamics. 3. Offers potential prognostic implications by associating T cell spatial distribution with patient survival and ICR transcriptome correlation. 1. Restricted to a specific cohort of colon cancer patients (n = 90), which could limit applicability to broader patient populations. 2. Relies on a specialized multiplex immunofluorescence assay, potentially introducing technical limitations and variability. 3. Requires additional validation and replication in larger cohorts to confirm the prognostic relevance of T cell spatial distribution in colon cancer Tumor-infiltrating T cell subtypes showed comparable densities, with proliferative and Granzyme B-expressing T cells located mainly within the tumor epithelium. Immune-active subtypes exhibited increased immune cell density and reduced distances between certain T cell subtypes and tumor cells, correlating with improved survival outcomes. [ 32 ] 3. Material and Methods The proposed method for diagnosing both cancer and non-cancer patients is designed to integrate several steps for the analysis of histopathological images. Initially, the method involves gathering and pre-processing sample images from a dataset of colon diseases. The pre-processing step includes noise elimination, adjustment, and image quality enhancement techniques such as histogram balancing. The research employs color images with a light intensity range of 0 to 255 for each channel. The proposed methodology combines the Fishier Mantis Optimizer (FMO) algorithm with convolutional neural networks (CNNs) to enhance the accuracy and reliability of colon cancer diagnosis. This integration aims to improve model performance and interpretability by optimizing feature selection during CNN training. The method utilizes convolutional neural networks based on GoogleNet and ResNet-50 for feature extraction from histopathological images. Textural features of the images are extracted using CNN methods, and essential features for machine learning are calculated. Feature selection is represented as a binary challenge, with a feature vector of n dimensions indicating the presence of n features, where each element is either zero or one. The FMO algorithm is employed for feature selection due to its ability to simulate learning behavior in convolutional algorithms, its superior accuracy compared to other meta-heuristic optimization methods, and its capability to perform global and local searches optimally. The subsequent stage involves training the machine learning algorithm using the optimal feature vector for the classification of cancer and non-cancer histopathological images. The proposed method also includes creating an optimal feature vector and employing machine learning for dimensionality reduction and classification. The final stage involves evaluating the proposed method using testing data. The phases of the proposed method for distinguishing between cancer and non-cancer colon images include collecting histopathological samples related to colon diseases. ■The samples are pre-processed to eliminate noise and enhance image quality. ■Feature extraction is performed using CNNs based on GoogleNet and ResNet-50. ■Textural features of the images are extracted using CNN methods. ■Essential features for machine learning are calculated from the images. ■ Feature selection is represented as a binary challenge with a feature vector of n dimensions. ■ The FMO algorithm is utilized for feature selection due to its superior accuracy and optimization capabilities. ■ The machine learning algorithm is trained using the optimal feature vector for classification. ■ Machine learning is employed for dimensionality reduction and classification of the images. ■ The proposed method is evaluated using testing data to assess its performance in distinguishing between cancer and non-cancer colon images. Overall, the proposed method integrates traditional data preprocessing techniques with innovative feature Diagnostics 2024,14, 1417 5 of 16 selection using the FMO algorithm and CNN training to enhance the accuracy and reliability of colon cancer diagnosis. The proposed methodology’s framework for the diagnosis of patients with colon cancer is shown in Figure 1. The visual representation of the model conceptual framework in Figure 2illustrates the seamless integration of these components, highlighting the novel approach taken in this research. Diagnostics 2024, 14, x FOR PEER REVIEW 7 of 18 Figure 1. The proposed methodology’s framework for the diagnosis of patients with colon cancer. Pre-processing histopathological images from noise cancellation with filtering methods Improve histopathological dataset images quality by balancing the histogram and smoothing the image size A feature vector is used to train the machine learning Reduce sample size and machine learning input Feature extraction by CNN based on GoogleNet and ResNe t -50 models Multiple feature vectors are created as members within the random FMO algorithm, and these feature vectors are using to determine machine learning input. Feature vectors are evaluated with classification accuracy and number of selected features Selection of optimal feature vectors from FMO Update feature vectors with training and learning phases Binarize feature vectors with FMO parameter A unit is adding to the counter and the parameters are initializing. Update? Yes No The optimal feature vector is using for machine learning and classification of cancer and non-cancer colon patients. Cancer Non-cancer Evaluation of the proposed approach through accuracy and classification error metrics for both cancer and non-cancer individuals Every feature vector is considered as a member of FMO algorithm load the histopathological images from the dataset Test samples Figure 1. The proposed methodology’s framework for the diagnosis of patients with colon cancer. Diagnostics 2024,14, 1417 6 of 16 Diagnostics 2024, 14, x FOR PEER REVIEW 8 of 18 Input Layer accepts histopathological images as pixel intensities in matrix form 1 Convolutional Layers extract features using specialized filters and activation functions like ReLU. Pooling Layers downsize feature maps to reduce complexity and overfitting through max or average pooling. Fully Connected Layers classify learned features after flattening, with variable neuron numbers. Output Layer interprets features for binary (cancer vs. non-cancer) or multi-class classification using activation functions like sigmoid or softmax. The CNN specializes in analyzing histopathological images for malignancy features, integrated with the Fishier Mantis Optimizer (FMO) for enhanced efficacy. The synergy of deep learning in CNNs and FMO's optimization enhances accuracy and efficiency in colon cancer diagnosis, capturing relevant features and reducing computational complexity. 2 3 4 5 6 7 Figure 2. Convolutional neural network for colon cancer diagnosis. Fishier Mantis Optimizer Algorithm The fisher mantis exhibits intelligent hunting behaviors, considering various scenarios and adjusting its position accordingly. It seeks the optimal location for prey or fish. Additionally, the fisher mantis displays uniform behaviors, including preparations for attacking or abandoning the current hunting state. The proposed method makes use of the FMO algorithm outlined in reference [32]. This algorithm systematically advances through iterations, gradually bringing the mantis closer to its prey. Through this process, the algorithm progressively narrows down the potential scenarios, as illustrated in Equation (1). Here, the parameter “m” signifies the initial states, while “t” represents the states at the current iteration stage. 𝑚󰇛𝑡󰇜= 𝑚−𝑚·𝑖𝑡 𝑀𝑎𝑥𝐼𝑡 (1) The feature vector 𝑋  will be used in machine learning for the classification of colon images into cancerous and non-cancerous categories. The dataset utilized in this study, “Lung and Colon Cancer Histopathological Images,” was sourced from an open-access dataset library available at “https://www.kaggle.com/datasets/andrewmvd/lung-and-colon-cancer-histopathological-images”, accessed on 10 February 2021. It comprises 25,000 histopathology images categorized into five classes. Each image is saved in JPEG format with dimensions of 768 × 768 pixels. Figure 2. Convolutional neural network for colon cancer diagnosis. Fishier Mantis Optimizer Algorithm The fisher mantis exhibits intelligent hunting behaviors, considering various scenarios and adjusting its position accordingly. It seeks the optimal location for prey or fish. Additionally, the fisher mantis displays uniform behaviors, including preparations for attacking or abandoning the current hunting state. The proposed method makes use of the FMO algorithm outlined in reference [ 32 ]. This algorithm systematically advances through iterations, gradually bringing the mantis closer to its prey. Through this process, the algorithm progressively narrows down the potential scenarios, as illustrated in Equation (1). Here, the parameter “m” signifies the initial states, while “t” represents the states at the current iteration stage. m(t)=m−m·it MaxIt (1) The feature vector Xnew i will be used in machine learning for the classification of colon images into cancerous and non-cancerous categories. The dataset utilized in this study, “Lung and Colon Cancer Histopathological Images,” was sourced from an open-access dataset library available at “https://www.kaggle.com/ datasets/andrewmvd/lung-and-colon-cancer-histopathological-images”, accessed on 10 February 2021. It comprises 25,000 histopathology images categorized into five classes. Each image is saved in JPEG format with dimensions of 768 ×768 pixels. Diagnostics 2024,14, 1417 7 of 16 Augmentation Procedure: To augment the dataset and increase its diversity, various augmentation techniques were applied to the original images. The augmentation process was implemented using the Augmentor package, which provides a flexible framework for image augmentation. The following augmentation techniques were utilized: 1. Rotation: ■ Range: Images were rotated within a range of angles to simulate variations in orientation. ■Angle range: [−15◦, 15◦]. 2. Translation: ■ Shift: Images were shifted horizontally and vertically to simulate translations. ■Shift range: [−20%, 20%] of image width and height. 3. Scaling: ■Scale factor: Images were scaled to simulate changes in size. ■Scale range: [0.8, 1.2]. 4. Cropping: ■ Random cropping: Portions of the images were randomly cropped to simulate variations in composition. ■Crop size: Images were cropped to a size of 700 ×700 pixels. 5. Flipping: ■ Horizontal flipping: Images were flipped horizontally to simulate mirror reflections. ■ Vertical flipping: Images were flipped vertically to introduce additional variations. By applying these augmentation techniques with specified parameters, the dataset was augmented to a total of 25,000 images, ensuring a diverse representation of histopathological features. This augmented dataset was then used for training and evaluating the proposed method for colon cancer detection. The classification task for colon images involved distinguishing between cancerous and non-cancerous classes. The dataset consisted of 25,000 histopathology images divided into five distinct categories, with each category containing 5000 images. It effectively conveys information about the classification task and the dataset related to colon images. Additionally, for clarity and visualization purposes, six examples of histopathological images from the dataset are provided below. Images prefixed with “colon_n_” indicate healthy colon tissue images, while those prefixed with “colon_ca_” depict images of colon cancer. Refer to Figure 3for a visual representation of these augmented images. Histopathological Features and Classifications: Histopathology entails the examination of tissue samples under a microscope, often obtained through biopsies, where minuscule tissue fragments are extracted and meticulously analyzed by pathologists. This thorough examination is instrumental in identifying both cancerous and pre-cancerous cellular abnormalities. Apart from colon cancer, the colon can be affected by a range of other conditions, underscoring the importance of histopathological analysis in reaching a definitive diagnosis. Diagnostics 2024,14, 1417 8 of 16 Diagnostics 2024, 14, x FOR PEER REVIEW 10 of 18 Figure 3. Exemplar pictures from the dataset. 4. Results and Discussion 4.1. Classification Using Learnable Classifiers for FMO To determine the ideal combination of techniques, a thorough investigation has been carried out. An autoencoder method and the FMO algorithm were employed collaboratively on datasets associated with colon disease to isolate and choose the most critical attributes from the input training dataset. The identical datasets used in the first model were categorized using a pre-trained CNN in conjunction with the FMO method. Some important metrics, like accuracy, F1-Score, etc., are applied to assess the effectiveness of methods created from the confusion matrix. For multiclass classification, metrics such as total accuracy, true positive rate, and false positive rate were considered. The fundamental terms used in this analysis include False Positive (FP), True Positive (TP), True Negative (TN), and False Negative (FN), which stand for positive and negative classifications, respectively. These indicators have been used to calculate accuracy (ACC), sensitivity (True Positive Rate (TPR)), specificity (True Negative Rate (TNR)), precision (positive predictive value (PPV)), negative predictive value (NPV), and F1-Score as follows: Accurac y = TP  TN TP  TN  FP  FN  10 (2) S ensitivit y  True Positive Rate 󰇛 TPR 󰇜 =TP TP  FN  100 (3) Speciicit y 󰇛True Negative Rate󰇛TNR󰇜󰇜 = TN TN  FP  100% (4) Precision 󰇛positive predictive value 󰇛PPV󰇜󰇜 = TP TP  FP  100% (5) Negative predictive value 󰇛NPV󰇜 = TN TN  FN  100% (6) F1 − score = 2PPV  TPR PPV  TPR  100% (7) Figure 3. Exemplar pictures from the dataset. 4. Results and Discussion 4.1. Classification Using Learnable Classifiers for FMO To determine the ideal combination of techniques, a thorough investigation has been carried out. An autoencoder method and the FMO algorithm were employed collaboratively on datasets associated with colon disease to isolate and choose the most critical attributes from the input training dataset. The identical datasets used in the first model were categorized using a pre-trained CNN in conjunction with the FMO method. Some important metrics, like accuracy, F1-Score, etc., are applied to assess the effectiveness of methods created from the confusion matrix. For multiclass classification, metrics such as total accuracy, true positive rate, and false positive rate were considered. The fundamental terms used in this analysis include False Positive (FP), True Positive (TP), True Negative (TN), and False Negative (FN), which stand for positive and negative classifications, respectively. These indicators have been used to calculate accuracy (ACC), sensitivity (True Positive Rate (TPR)), specificity (True Negative Rate (TNR)), precision (positive predictive value (PPV)), negative predictive value (NPV), and F1-Score as follows: Accuracy =TP +TN TP +TN +FP +FN ×10 (2) Sensitivity(True Positive Rate(TPR)) =TP TP +FN ×100 (3) Specificity (True Negative Rate (TNR)) = TN TN +FP ×100% (4) Precision (positive predictive value (PPV)) = TP TP +FP ×100% (5) Negative predictive value (NPV) = TN TN +FN ×100% (6) F1 −score =2PPV ×TPR PPV +TPR ×100% (7) 4.2. Using Auto-Encoder with FMO for Colon Disease Dataset Various scenarios were developed and assessed to validate the effectiveness of the proposed technique and compare different combinations. In the initial stage, a dataset related to colon illness was processed by the autoencoder, along with five different classifier Diagnostics 2024,14, 1417 9 of 16 types. The outcomes of the colon illness dataset using the autoencoder and the FMO technique are displayed in Table 2. Table 2. Dataset on colon disease auto-encoder using FMO feature selection algorithm. Method ACC TPR TNR PPV NPV F1-Scoce Decision Tree 67.80 67.94 67.94 68.20 68.20 67.93 SVM 68.40 73.83 73.83 79.80 79.80 71.63 KNN 75.35 77.05 77.05 78.50 78.50 76.10 Ensemble 73.00 74.63 74.63 76.30 76.30 73.86 Naive Bayes 66.60 72.74 72.74 80.10 80.10 70.57 The most crucial factor in assessing this classification model is accuracy, which is based on the true values of the tested images that were classified. The KNN classifier achieved a higher accuracy rate of 75.35%. 4.3. Pre-Trained CNN with FMO for Colon Disease Dataset The assessment of pre-trained CNN with FMO was conducted using the dataset related to colon diseases. The simulation outcome, employing the FMO method in conjunction with the pre-trained ResNet-50 network, is presented in Figure 4. Diagnostics 2024, 14, x FOR PEER REVIEW 11 of 18 4.2. Using Auto-Encoder with FMO for Colon Disease Dataset Various scenarios were developed and assessed to validate the effectiveness of the proposed technique and compare different combinations. In the initial stage, a dataset related to colon illness was processed by the autoencoder, along with five different classifier types. The outcomes of the colon illness dataset using the autoencoder and the FMO technique are displayed in Table 2. Table 2. Dataset on colon disease auto-encoder using FMO feature selection algorithm. Method ACC TPR TNR PPV NPV F1-Scoce Decision Tree 67.80 67.94 67.94 68.20 68.20 67.93 SVM 68.40 73.83 73.83 79.80 79.80 71.63 KNN 75.35 77.05 77.05 78.50 78.50 76.10 Ensemble 73.00 74.63 74.63 76.30 76.30 73.86 Naive Bayes 66.60 72.74 72.74 80.10 80.10 70.57 The most crucial factor in assessing this classification model is accuracy, which is based on the true values of the tested images that were classified. The KNN classifier achieved a higher accuracy rate of 75.35%. 4.3. Pre-Trained CNN with FMO for Colon Disease Dataset The assessment of pre-trained CNN with FMO was conducted using the dataset related to colon diseases. The simulation outcome, employing the FMO method in conjunction with the pre-trained ResNet-50 network, is presented in Figure 4. Figure 4. Result of the simulation based on the ResNet-50 and FMO. Figure 4 illustrates that the accuracy of the DT, SVM, KNN, and ensemble methods has been achieved at 90.82%, 95.01%, 95.04%, and 93.46%, respectively. The KNN classification method achieved 95.04% accuracy, which was the best result. KNN is a non-parametric supervised learning classifier that is more accurate than other methods such as SVM, decision trees, and ensemble methods. Additionally, in this scenario, the highest accuracy was achieved using the KNN classifier with features that were obtained using 0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 Accuracy TPR TNR PPV NPV F1Score Decision Tree 90.82 89.35 90.09 83.49 88.09 68.54 SVM 95.01 95.12 94.63 94.21 95.27 94.25 KNN 95.04 97.74 82.95 97.74 96.91 97.74 Ensemble 93.46 95.41 94.54 94.53 95.42 94.97 Figure 4. Result of the simulation based on the ResNet-50 and FMO. Figure 4illustrates that the accuracy of the DT, SVM, KNN, and ensemble methods has been achieved at 90.82%, 95.01%, 95.04%, and 93.46%, respectively. The KNN classification method achieved 95.04% accuracy, which was the best result. KNN is a non-parametric supervised learning classifier that is more accurate than other methods such as SVM, decision trees, and ensemble methods. Additionally, in this scenario, the highest accuracy was achieved using the KNN classifier with features that were obtained using the FMO algorithm and a pre-trained ResNet-50 network. Additionally, this study assessed the performance of decision tree, SVM, KNN, and ensemble methods with the F1-Score metric, yielding scores of 68.54%, 94.25%, 97.74%, and 94.97% for these algorithms, respectively. As seen from the F1-Score results, it can be understood that the KNN has higher accuracy than other methods. Diagnostics 2024,14, 1417 16 of 16 27. Nisha, J.S.; Gopi, V.P.; Palanisamy, P. Automated Colorectal Polyp Detection Based on Image Enhancement and Dual-Path CNN Architecture. Biomed. Signal Process. Control 2022,73, 103465. [CrossRef] 28. Nanni, L.; Fantozzi, C.; Loreggia, A.; Lumini, A. Ensembles of Convolutional Neural Networks and Transformers for Polyp Segmentation. Sensors 2023,23, 4688. [CrossRef] 29. 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