Jul 2026· LAUTECH Journal of Engineering and Technology· 0 citations
TL;DR
A brain tumor classification system integrated with Explainable Artificial Intelligence (XAI) was developed using MRI images and demonstrated effective classification performance and improved interpretability, making it suitable for automated brain tumor diagnosis.
Abstract
Brain tumors are abnormal cell growths in the brain that require accurate and timely diagnosis, where Magnetic Resonance Imaging (MRI) plays a critical role in detecting and characterizing tumor structures. However, accurate interpretation of Magnetic Resonance Imaging (MRI) scans is challenging due to their complexity and the limited availability of expert radiologists. This challenge is further compounded by the lack of interpretability in many existing deep learning-based diagnostic systems. Therefore, the need for an automated and interpretable brain tumor classification system arises, which is the problem this study aims to address. In this research, a brain tumor classification system integrated with Explainable Artificial Intelligence (XAI) was developed using MRI images. The model was designed to classify brain tumors into glioma, meningioma, pituitary tumor, and no-tumor categories while providing visual explanations for its predictions using Grad-Class Activation Mapping (Grad-CAM). The performance of the system was evaluated for each tumor category using accuracy, precision, specificity, recall, F1-Score, false positive rate and also the Receiver Operating Characteristic and Area Under Curve (ROC-AUC). Experimental results show that the developed CNN model achieved an overall classification accuracy of 90.6% with an AUC-ROC value of 0.9892, indicating strong discriminative capability across tumor classes. The Grad-CAM visualizations consistently highlighted tumor-affected regions in the MRI images, confirming that the model based its predictions on clinically relevant anatomical structures. The developed model demonstrated effective classification performance and improved interpretability, making it suitable as a reliable decision-support tool for automated brain tumor diagnosis
This study investigates the application of deep learning architectures, including Convolutional Neural Network, VGG16, VGG19, ResNet50, and MobileNet, for brain tumor detection and classification and confirms that advanced deep learning architectures not only achieve high classification accuracy but also improve interpretability, thereby offering reliable and clinically applicable solutions for automated brain tumor diagnosis.
H. Uzel, Feyyaz Alpsalaz, Yıldırım Özüpak et al.· Computers and Electronics in...· 0 citations
The recent development in the field of artificial intelligence and digital medical imaging has also disrupted the process of analysis and diagnosis of neurological disorders. The automated examination of brain MRI scans has gained more significance in helping radiologists to diagnose tumors with high accuracy as well as decrease the time and manual work involved in diagnosis. The problem of brain tumor detection and classification is not resolved yet, as each patient has different tumors, and some have different shapes, sizes, and intensity. The proposed project will resolve these problems by implementing deep learning based object detection models to tackle medical image analysis with an automated classifier of brain tumors and their rate of growth with the help of the YOLOv8 deep learning model. Preprocessing of MRI images involves a preprocessing pipeline comprising of resizing and normalization of the aspect ratio to enhance localization of tumors to be used as a bounding box in detecting them. Brain tumors are recognized as four types; glioma, meningioma, pituitary tumor and no tumor (YOLOv8 model). The system provides the results of prediction as the type of tumor, the segmented part of the tumor, and the confidence score. The size of the tumors is estimated by the area identified and the rate of growth is calculated based on the analysis carried out by medical rules. The experimental findings prove that the suggested deep learning model not only offers effective tumor detection and classification but also effective growth evaluation, which makes it appropriate as a clinical decision support system in the field of early diagnosis and regular follow-ups.
M. Parameswari, R. D, S. S et al.· International Conference Com...· 0 citations
The proposed explainable deep learning framework shows great promise of helping clinical diagnosis of brain tumors to be reliable and transparent, by integrating with AI.
Mohd. Yousuf, Joy Chowdhury, Susmoy Chowdhury et al.· American Journal of Applied...· 0 citations
The study adds a rigorous benchmarking mechanism and empirical evidence for adopting ResNet50 as a robust model for multi-class brain tumour diagnosis and highlights the power of deep residual learning for solving some of the difficulties in classifying brain MRI, such as inter-class similarity and feature heterogeneity.
Prabha Kumaresan, Xin Tian Lim· International Journal on Rob...· 0 citations
Brain tumors affect millions of patients worldwide. Magnetic resonance imaging (MRI) is the preferred detection modality due to its radiation-free nature, high soft-tissue contrast, and three-dimensional (3D) imaging capabilities. However, the 3D complexity of MRI scans makes manual classification time-consuming, inefficient, and prone to errors. Consequently, developing high-precision automated classification is crucial in neurology. This study proposes BrainTumor CNN, a convolutional neural network (CNN) for classifying brain tumor MRI images. It leverages transfer learning via a pre-trained ResNet-18 network, integrating data augmentation and Dropout regularization to enhance robustness and generalization. The model demonstrates exceptional diagnostic performance, achieving 98.6% accuracy, with precision, recall, and F1-score all reaching 99.8%. By balancing high accuracy with low computational cost, this study provides an efficient diagnostic tool suitable for real-time clinical deployment.
Yicheng Xu· International Conference on...· 0 citations
Abstract Introduction Pediatric cancer imposes a significant global burden, with ∼400 000 new cases annually. Magnetic resonance imaging (MRI) is a fundamental technique for timely diagnosis; however, interpretation can be subjective. In this context, convolutional neural networks (CNNs) have emerged as a promising tool for early and accurate detection of brain tumors in pediatric patients. Objective To develop and validate a CNN model for tumor detection tasks (tumor vs nontumor) of pediatric brain MRI images using T1-weighted imaging. Methodology T1-weighted brain MRI images from 285 pediatric patients were included (140 with confirmed tumors and 145 controls). A CNN architecture with convolutional layers, max-pooling, and dropout regularization was implemented. The model was trained and evaluated using cross-validation, employing the following metrics: accuracy, precision, recall, and F1-score. Model performance was compared across training configurations of 10-50 and 100 epochs to determine the optimal setup. Results The model trained for 40 epochs achieved the best overall performance, with a precision of 99%, a recall of 100%, and an F1-score of 99%. These metrics demonstrate excellent tumor detection while eliminating false negatives, a crucial aspect in pediatric oncology. Conclusion The CNN demonstrates strong potential as a diagnostic aid for detection brain tumors using pediatric MRI images. Its integration into clinical environments could contribute to more objective interpretation, optimize diagnostic workflows, and provide imaging follow-up before and after treatment.