Jul 2026· International Journal on Robotics Automation and Sciences· 0 citations· 13 references
TL;DR
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.
Abstract
Brain tumors represent an important clinical disease of the nervous system that requires early and accurate diagnosis for improved patient prognoses. Magnetic Resonance Imaging (MRI) is still the modality of choice for the evaluation of brain tumor due to its better soft tissue contrast and non-invasive nature. Nevertheless, manual interpretation of MRI examinations is difficult and subject to inter-observer variation. The present study aims at a thorough comparative evaluation of deep learning architectures for fully automated, multi-class brain tumor classification based on MRI data. A unified experimental framework is designed to empirically evaluate a custom convolutional neural network (CNNs) trained with the scratch method and four pre-trained transfer learning methods, including VGG16, ResNet50, DenseNet121, and MobileNetV3. The models are trained and validated using a large, consolidated data set of four classes: glioma, meningioma, pituitary tumour and healthy brain images. In order to make sure the comparability between models is true, same preprocessing, data augmentation and dataset partition strategy and the evaluation measure are followed by all the models. Performance is measured in terms of accuracy, precision, recall, F1 score, confusion matrices and roc-auc analysis. Results show that transfer learning models significantly exceed the custom CNN. Among the architectures evaluated, ResNet50 can achieve the best classification performance, test accuracy is 98.38% and macro-averaged F1-score is 0.9837, which can be regarded as good classification and generalisability. These results highlight the power of deep residual learning for solving some of the difficulties in classifying brain MRI, such as inter-class similarity and feature heterogeneity. The study adds a rigorous benchmarking mechanism and empirical evidence for adopting ResNet50 as a robust model for multi-class brain tumour diagnosis. Future research may extend this work by develop a web-based application that enables users to upload MRI images for automated brain tumor detection and classification.
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
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.
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
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
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
Magnetic resonance imaging (MRI) is a crucial component of the medical diagnostic and therapeutic approach for brain tumors, as early detection of anomalous tissue considerably enhances patient outcomes. MRI images may obscure significant tumor characteristics owing to noise, inadequate contrast, and erratic intensity distributions. This work examines the application of ResNet169, EfficientNetB0, and a hybrid fused model (ResNetEffi169B0) to address challenges in deep learning-based image improvement and categorization. The performance metrics included F1-score, Accuracy, Precision, Sensitivity, Entropy, SSIM, and MSE to test the models. The results show that EfficientNetB0 has the best PSNR (11.45), SSIM (0.308), and accuracy (0.601) when it comes to classification and improvement. The ResNet169 model's high sensitivity of 0.463 showed that it could dependably find tumor regions. The hybrid approach used the best features of both styles to create balanced results that made diagnoses more consistent and images clearer. The fusion method enhances the quality and structural data of magnetic resonance imaging (MRI) scan slices, leading to more precise classification and enhanced tumor visibility. This study examines the potential of hybrid deep learning models to enhance computer-aided diagnostic tools in medical imaging for improved brain tumor identification.
Balas, Subalatha.M· 2026 6th International Confe...· 0 citations