Jul 2026· Computers and Electronics in Medicine· 0 citations· 31 references
TL;DR
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.
Abstract
Brain tumors represent one of the most life-threatening diseases, and their early and accurate detection is critical for improving patient outcomes. Magnetic Resonance Imaging (MRI) is the most reliable imaging technique for identifying brain tumors, yet manual interpretation by radiologists is time-consuming and prone to errors. To address these challenges, this study investigates the application of deep learning architectures, including Convolutional Neural Network (CNN), VGG16, VGG19, ResNet50, and MobileNet, for brain tumor detection and classification. A publicly available MRI dataset consisting of glioma, meningioma, pituitary, and no-tumor cases was used. The models were trained and evaluated using accuracy, precision, recall, F1-score, ROC curves, and confusion matrices, while interpretability was assessed using Local Interpretable Model-Agnostic Explanations (LIME). Experimental results demonstrate that ResNet50 achieved the highest performance with 96.9% accuracy, followed closely by MobileNet at 96.6%, whereas CNN performed less effectively at 87.9%. The findings confirm 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.
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.
T. H. Stephen, A. Oke, A. S. Falohun et al.· LAUTECH Journal of Engineeri...· 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
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.
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 tumor classification plays an important role in early diagnosis and treatment planning. The current study aims to evaluate and compare the performance of five pre-trained convolutional neural network (CNN) models, namely VGG16, VGG19, MobileNet, Xception, and InceptionV3 using magnetic resonance imaging (MRI) images categorized into glioma, meningioma, pituitary tumor, and no tumor classes. To enhance model performance and address class imbalance, transfer learning and data augmentation techniques were employed. To boost model interpretability, heatmaps of important areas in tumor classification were produced through gradient-weighted class activation mapping (Grad-CAM). MobileNet was the most accurate with 97% and was more precise and more sensitive. The Grad-CAM visualizations showed the models were attending to clinically relevant features, which increased the interpretability. This comparative study demonstrates the effectiveness of the integration of explainable artificial intelligence (XAI) in deep learning pipelines for reliable brain tumor diagnosis.
Md. Firoz Hasan, Md. Awal Hadi, S. Nasrin et al.· IAES International Journal o...· 0 citations