Year: 2025
Type: article
Abstract: — Abstract — Brain tumors are among the most life-threatening and complex conditions, requiring early and accurate detection to ensure effective treatment and improved survival rates. Manual interpretation of MRI scans for diagnosis is a labor-intensive process, demanding significant expertise and carrying the risk of human inaccuracies. This research proposes a deep learning-based framework using a Deep Convolutional Neural Network (DCNN) specifically instantiated with the pretrained ResNet50 model for the automated detection and classification of brain tumors from MRI images. MRI scans are processed into a classification system that identifies four conditions: glioma, meningioma, pituitary tumor, or a healthy (no tumor) state. We prepare the dataset for this task using normalization, resizing, and augmentation to improve model robustness and reduce the risk of overfitting. The DCNN specifically with pretrained ResNet50 architecture is designed with multiple convolutional, pooling, and dense layers to extract complex features and learn spatial hierarchies within the data. The model is trained using categorical cross-entropy loss and optimized via the Adam optimizer to achieve high classification accuracy. Extensive validation and testing show that the model achieves reliable performance with high precision and recall across all tumor types. The trained model is further integrated into a user-friendly web interface using the Flask framework, enabling real-time prediction from uploaded MRI scans. This system provides an accessible and effective diagnostic tool, especially beneficial in resource-constrained settings, and contributes significantly to the field of medical imaging and intelligent healthcare solutions. Keywords—Brain tumor detection, Deep Convolutional Neural Network, DCNN, ResNet50, medical imaging, MRI classification, Flask deployment.
DOI: 10.55041/ijsrem50618
Author: Dr. C. Srinivasa Kumar
Language: English
Subfield: Neurology
Field: Neuroscience
Domain: Life Sciences
Open Access status: bronze
OpenAlex ID: W4411326417