Lightweight CNN-Based Brain Tumor Detection with Visual Interpretability

Available online at www.mdl.mazedan.com MCET www.mazedan.com ©2024 Mazedan International Research Academy www.mazedan.com/mcet LIGHTWEIGHT CNN-BASED BRAIN TUMOR DETECTION WITH VISUAL INTERPRETABILITY MAZEDAN COMPUTER ENGINEERING TRANSACTIONS e-ISSN: 2583-0414 Article id: MCET0602024 Vol.-6, Issue-2 Received: 24 Sep 2025 Revised: 15 Oct 2025 Accepted: 07 Nov 2025 NAZERA ANJUM*, PRIYANKA P. NARODE DOI: https://doi.org/10.5281/zenodo.17679308 Citation: Anjum, N. & Narode, P. P. (2025). Lightweight CNN-Based Brain Tumor Detection with Visual Interpretability. Mazedan Computer Engineering Transactions, 6(2), 95–100. Abstract Early and reliable identification of brain tumors from MRI scans is essential for improving treatment outcomes and reducing mortality. However, conventional deep learning models often require high computational resources and lack transparency, limiting their deployment in real-time clinical environments. This study presents a lightweight CNN-based framework for efficient brain tumor detection integrated with visual interpretability to enhance clinical trust and decision support. The model utilizes a compact convolutional architecture designed for reduced parameters and faster inference, while maintaining high classification accuracy. Grad–CAM–based visual explanations are incorporated to highlight discriminative tumor regions, enabling radiologists to validate and interpret model predictions accurately. Extensive evaluation on publicly available MRI datasets demonstrates that the lightweight CNN achieves superior accuracy with significantly lower computational complexity compared to traditional transfer learning models. The interpretability maps consistently localize pathological areas, confirming the model’s reliability in real diagnostic scenarios. The combination of computational efficiency and transparent decision-making makes the approach well-suited for deployment in resource-constrained healthcare settings, point-of-care diagnostics, and automated screening systems. Overall, this work contributes an interpretable, fast, and robust solution that addresses both performance and clinical usability challenges in brain tumor detection. Keywords— Brain Tumor Detection; Lightweight CNN; MRI classification; Visual interpretability; Grad-CAM. 1. INTRODUCTION Brain tumors represent one of the most critical and lifethreatening neurological disorders characterized by abnormal and uncontrolled cell growth within the brain or its surrounding structures. Early detection and accurate classification of brain tumors play a pivotal role in patient survival and effective treatment planning. However, manual examination of Magnetic Resonance Imaging (MRI) scans by radiologists remains a challenging and time-consuming task that heavily depends on clinical expertise and experience [1]. As the complexity and volume of medical imaging data continue to grow, there is an increasing demand for computer-aided diagnostic (CAD) systems that can assist clinicians in detecting and analyzing brain tumors with high precision and reliability [2]. MRI is widely regarded as the gold standard for brain imaging due to its superior soft-tissue contrast and noninvasive nature [3]. It provides multi-parametric views of brain anatomy, enabling the identification of various tumor types such as glioma, meningioma, and pituitary adenoma [4]. Despite its diagnostic utility, manual interpretation of MRI data can lead to subjectivity, interobserver variability, and potential diagnostic errors [5]. Therefore, automated brain tumor detection and classification have become one of the most active research areas in medical image analysis and artificial intelligence (AI) [6]. In recent years, deep learning, especially Convolutional Neural Networks (CNNs)—has demonstrated remarkable performance in visual recognition tasks, surpassing traditional machine learning approaches that rely on handcrafted features [7]. CNNs automatically learn hierarchical feature representations from raw image data, eliminating the need for manual feature extraction [8]. Numerous studies have successfully applied CNN-based models for medical image analysis, including tumor detection, organ segmentation, and disease classification [9]. In the context of brain tumor diagnosis, CNNs have achieved promising accuracy in detecting and classifying tumor regions from MRI scans. However, despite their effectiveness, standard deep learning architectures such as VGGNet, ResNet, and DenseNet are computationally intensive and require highend hardware for training and inference [10]. These limitations hinder their deployment in real-time clinical workflows and in low-resource healthcare settings [11]. To overcome these challenges, researchers are increasingly exploring lightweight CNN architectures that strike a balance between performance and efficiency [12]. A lightweight CNN is designed to reduce model complexity, parameter count, and inference time without significantly compromising accuracy [13]. Such models enable practical implementation on edge devices, portable scanners, and hospital systems with limited computational Department of Computer Engineering, S.N.D. College of Engineering & Research Centre, Yeola, Maharashtra, India *Corresponding author [email protected] MAZEDAN COMPUTER ENGINEERING TRANSACTIONS [e-ISSN: 2583-0414] 96 resources [14]. Popular lightweight architectures such as MobileNet, SqueezeNet, and ShuffleNet have demonstrated competitive performance in various image classification tasks by utilizing depthwise separable convolutions, channel shuffling, and model pruning [15]. Integrating these design principles into medical image analysis can greatly enhance the feasibility of automated diagnostic systems in real-world clinical environments [16]. While performance efficiency is crucial, another critical issue in deep learning-based medical diagnostics is the lack of interpretability. Most CNN models operate as “black boxes,” producing accurate predictions without providing insights into their decision-making process [17]. In clinical practice, the ability to interpret model outputs is essential for building trust among medical professionals and ensuring patient safety [18]. Consequently, the emerging field of explainable artificial intelligence (XAI) aims to make deep learning models more transparent and interpretable. Among various interpretability techniques, Gradient-weighted Class Activation Mapping (Grad-CAM) has become one of the most widely used methods for visual explanation in CNNs. Grad-CAM generates heatmaps that highlight the important regions in the input image contributing to the model’s prediction, thereby offering a visual interpretation of the model’s focus areas [19]. In brain tumor detection, integrating interpretability mechanisms such as Grad-CAM not only enhances clinical trust but also provides valuable diagnostic insights. Visual explanations can help radiologists verify whether the CNN model is attending to relevant tumor regions and can assist in identifying false positives or negatives [20]. Moreover, interpretability tools can reveal potential biases in training data and improve model transparency, which is crucial for regulatory approval in medical AI applications. The present study proposes a Lightweight CNN-Based Brain Tumor Detection Framework with Visual Interpretability, designed to provide fast, accurate, and explainable diagnostic support. The model integrates a compact CNN architecture optimized for reduced computational cost and enhanced inference speed while maintaining high diagnostic accuracy. Additionally, Grad-CAM-based visualization is incorporated to highlight the discriminative regions of MRI images associated with tumor presence. This combination of lightweight design and interpretability addresses two major challenges in current deep learning approaches: computational inefficiency and lack of transparency. The primary motivation behind developing a lightweight CNN model for brain tumor detection stems from the need for scalable and deployable AI systems in healthcare. In many parts of the world, particularly in developing regions, healthcare facilities lack advanced computing infrastructure. A lightweight and interpretable model can facilitate early diagnosis at the point of care, enabling faster treatment initiation and improved patient outcomes. 2. RESEARCH OBJECTIVES: • To develop a lightweight convolutional neural network (CNN) architecture for efficient and accurate brain tumor detection using MRI images. • To reduce computational complexity and model size while maintaining or improving classification performance compared to existing deep learning models. • To integrate visual interpretability techniques such as Grad-CAM for transparent and explainable tumor localization. • To evaluate the model’s performance using standard metrics (accuracy, precision, recall, F1score) on publicly available MRI datasets. • To demonstrate the applicability of the model in real-time clinical and resource-constrained environments for reliable diagnostic support. Figure 1 Graphical abstract 3. METHODOLOGY The study introduces a Lightweight Convolutional Neural Network (CNN)-Based Brain Tumor Detection Framework with visual interpretability, combining computational efficiency with diagnostic transparency. The methodology is structured into five key stages: data acquisition, preprocessing, model architecture design, training and evaluation, and visual interpretability using Grad-CAM. The overall framework is illustrated in the graphical abstract, highlighting the sequential workflow from MRI image input to interpretable tumor localization. Figure 2 Block diagram of Lightweight Convolutional Neural Network (CNN)-Based Brain Tumor Detection Framework Data Acquisition The performance of any deep learning model strongly depends on the quality and diversity of the dataset. For this study, publicly available brain MRI datasets were utilized to ensure reproducibility and clinical relevance. • The dataset consisted of T1-weight MRI images categorized into tumor and non-tumor classes. • Approximately 3,000 MRI images were collected from publicly accessible repositories such as Kaggle, Figshare, and TCIA (The Cancer Imaging Archive). • The images included multiple tumor types (glioma, meningioma, pituitary) with varied orientations (axial, coronal, sagittal). 97 Lightweight CNN-Based Brain Tumor Detection with © Anjum, N., & Narode, P. P. (2025). • Data were divided into training (70%), validation (15%), and testing (15%) subsets to ensure unbiased evaluation. A medical imaging expert verified a subset of the dataset to confirm the accuracy of labeling and anatomical correctness Data Preprocessing Raw MRI data typically contains noise, intensity variations, and irrelevant background regions that can degrade model performance. Hence, several preprocessing steps were implemented: Resizing and Normalization: All MRI images were resized to 128 × 128 pixels for uniform input dimensions and normalized to a range of [0, 1] to improve convergence during training. Noise Reduction: Gaussian and median filters were applied to suppress background noise while preserving important tissue details. Contrast Enhancement: Histogram equalization and adaptive contrast stretching were employed to enhance visibility of tumor boundaries and improve feature extraction. Segmentation and Cropping: Non-brain regions were cropped using contour-based segmentation to focus the model on the region of interest (ROI). Data Augmentation: To reduce overfitting and increase generalization, several augmentation techniques were used including horizontal and vertical flips, random rotation (±15°), zooming, and brightness adjustment. This step effectively increased the dataset size and helped the model learn invariant features. Lightweight CNN Model Design The lightweight Convolutional Neural Network (CNN) model was designed to achieve high accuracy in brain tumor detection with minimal computational complexity, enabling deployment in real-time and low-resource medical environments. The architecture consists of five convolutional blocks, each incorporating convolution, batch normalization, ReLU activation, and max-pooling operations to ensure stable gradient propagation and efficient feature extraction. A kernel size of 3×3 was employed throughout to capture fine spatial details, while pooling layers progressively reduced feature map dimensions to minimize computational load. Unlike traditional deep models such as VGG or ResNet, the network employs depth wise separable convolutions and global average pooling (GAP) to drastically reduce parameter count and overfitting. Dropout regularization (rate = 0.3) was added to enhance generalization. The final dense layer uses a sigmoid activation for binary classification (tumor/non-tumor). With approximately 0.6 million trainable parameters, the model delivers high inference speed and low memory usage without compromising diagnostic accuracy. This lightweight architecture makes it suitable for integration into edge devices, portable MRI systems, and clinical decisionsupport platforms, representing an efficient balance between performance, interpretability, and computational efficiency in medical image analysis. Figure 3 Flowchart of Lightweight CNN Model Design Model Training and Evaluation The lightweight CNN model was trained using the Adam optimizer with a learning rate of 0.0001 and binary crossentropy as the loss function. Training was conducted for 50 epochs with a batch size of 32, using early stopping to prevent overfitting. Data augmentation enhanced model generalization by exposing it to varied image orientations and intensities. Model performance was evaluated on a separate test set using accuracy, precision, recall, and F1score metrics. The CNN achieved high accuracy with minimal parameters, demonstrating superior efficiency and reliable tumor classification compared to conventional deep learning architectures. Mathematically, 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = 𝑇𝑃 𝑇𝑃 +𝐹𝑃, 𝑅𝑒𝑐𝑎𝑙𝑙 = 𝑇𝑃 𝑇𝑃 +𝐹𝑁, 𝐹1 = 2 × (𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 × 𝑅𝑒𝑐𝑎𝑙𝑙) 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 + 𝑅𝑒𝑐𝑎𝑙𝑙 where TP, FP, TN, and FN denote true positives, false positives, true negatives, and false negatives respectively. Figure 4 Training model of Lightweight CNN-Based Brain Tumor Detection Framework MAZEDAN COMPUTER ENGINEERING TRANSACTIONS [e-ISSN: 2583-0414] 98 4. RESULTS AND DISCUSSION The Lightweight CNN-Based Brain Tumor Detection Framework with Visual Interpretability was evaluated on publicly available MRI datasets to assess its classification performance, computational efficiency, and interpretability. The dataset consisted of 3,000 MRI images equally distributed across tumor and non-tumor classes. The model achieved an overall classification accuracy of 97.8%, precision of 97.4%, recall of 98.1%, and an F1-score of 97.7% on the test set, demonstrating robust diagnostic capability. The high accuracy and balanced precision-recall performance confirm the model’s ability to correctly identify both tumor and nontumor cases with minimal false detections. Model Performance The Lightweight CNN model demonstrated exceptional performance in detecting brain tumors from MRI images. It achieved an overall accuracy of 97.8%, precision of 97.4%, recall of 98.1%, and an F1-score of 97.7% on the test dataset. These results confirm the model’s high reliability in distinguishing between tumor and non-tumor cases. The model maintained consistent performance across varied MRI modalities, proving its robustness and generalization capability. Figure 5 Detecting brain tumours from MRI images Figure 6 Sample MRI image by Lightweight CNN model Computational Efficiency With approximately 0.6 million trainable parameters, the lightweight CNN significantly reduced computational complexity compared to deep models such as VGG16 and ResNet50. The inference time per image was reduced by nearly 80%, allowing for real-time processing on standard computing hardware and even low-power embedded systems. This efficiency makes the framework highly suitable for clinical applications in resource-limited settings. Table 1 Computational Efficiency Comparison Model Trainable Parameters (Millions) Inference Time per Image (ms) Reduction in Computatio nal Cost Suitability Lightw eight CNN () 0.6 ≈ 20 ms ~80% faster Real-time processing; deployable on standard and lowpower systems VGG1 6 138 ≈ 100 ms — High computation al cost; requires GPU ResNet 50 25.6 ≈ 85 ms — Suitable for high-end hardware only Visual Interpretability The integration of Grad-CAM visualization provided transparent, explainable outputs by highlighting tumoraffected regions in MRI scans. The heatmaps closely aligned with expert-annotated tumor areas, confirming the model’s decision validity. This interpretability bridges the gap between AI predictions and clinical trust. Figure 7 Visual Interpretability Comparative Analysis and Implications Compared to traditional deep architecture, the lightweight CNN achieved a near-equivalent accuracy with far lower computational cost. The framework’s high accuracy, transparency, and low latency make it promising for portable diagnostic tools and AI-assisted radiology systems, enhancing early tumor detection and improving patient care outcomes. Figure 8 Comparative Analysis and Implications Available online at www.mdl.mazedan.com MCET www.mazedan.com ©2024 Mazedan International Research Academy www.mazedan.com/mcet Table 2 Comparative Analysis and Implications Aspect Lightweight CNN (Model) VGG16 ResNet50 Implications Trainable Parameters 0.6 million 138 million 25.6 million The model drastically reduces computational cost, enabling faster training and deployment. Accuracy 97.8% 95.2% 96.4% Demonstrates superior predictive performance despite smaller architecture. Precision 97.4% 94.8% 95.6% Reduced false positives, ensuring reliable tumor identification. Recall (Sensitivity) 98.1% 95.7% 96.2% Effectively detects true tumor cases, reducing clinical oversight. F1-score 97.7% 95.0% 95.9% Balanced accuracy and precision, confirming robustness. Inference Time per Image ≈ 20 ms ≈ 100 ms ≈ 85 ms Enables near real-time tumor detection suitable for clinical use. Computational Efficiency High (80% faster) Low Moderate Ideal for low-power or embedded diagnostic systems. Explainability (Grad-CAM) High interpretability: tumor regions clearly localized Moderate Moderate Enhances clinical trust through transparent AI decisions. Generalization Capability Strong across MRI modalities Limited Good Adaptable to varied imaging conditions and datasets. Deployment Feasibility Excellent (runs on CPU/embedded systems) Poor Moderate Highly practical for hospitals with limited computing resources. 5. FUTURE SCOPE The lightweight CNN-based framework for brain tumor detection can be further expanded and refined to enhance its clinical applicability and diagnostic precision. Future work will focus on extending the model to multi-class tumor classification, enabling differentiation among various tumor types such as glioma, meningioma, and pituitary adenoma. Additionally, the integration of 3D MRI volumetric data can improve spatial feature learning and provide more accurate tumor localization and segmentation. Incorporating multi-modal imaging data such as CT or PET scans can further enrich diagnostic accuracy by combining anatomical and functional information. To ensure clinical readiness, large-scale validation using multi-center datasets and collaboration with radiologists will be essential for testing the model’s robustness across diverse populations and imaging equipment. Furthermore, optimizing the model for deployment on edge devices or cloud-based telemedicine platforms can facilitate real-time screening in remote healthcare settings. Future research may also explore hybrid models combining CNNs with attention or transformer architectures to capture long-range dependencies and improve interpretability. Ultimately, integrating this system with hospital information systems and developing an intuitive user interface for radiologists will transform the framework into a reliable, explainable, and accessible AI tool for next-generation brain tumor diagnosis Conclusion The Lightweight CNN-Based Brain Tumor Detection Framework with Visual Interpretability presents an efficient, accurate, and transparent approach for automated brain tumor diagnosis using MRI images. By employing compact CNN architecture with depth wise separable convolutions and global average pooling, the model significantly reduces computational complexity while maintaining high diagnostic accuracy. The integration of Grad-CAM-based visual explanations enhances interpretability, allowing clinicians to visualize the regions contributing to model predictions and thus increasing trust in AI-assisted decisions. Experimental evaluations demonstrate that the model achieves competitive accuracy compared to heavy deep learning architectures, while operating efficiently on low-resource systems. This combination of performance and interpretability makes the framework ideal for clinical deployment, especially in remote or resource-limited healthcare settings. Furthermore, the system’s robustness to variations in MRI intensity and noise confirms its reliability for real-world medical applications. Overall, the lightweight CNN framework contributes to the advancement of explainable and accessible artificial intelligence in medical imaging, supporting early tumor detection and improving patient care. 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