Employing Xception convolutional neural network through high-precision MRI analysis for brain tumor diagnosis

• Published in: Frontiers in medicine 2024-11, Vol.11, p.1487713
• Main Authors: Sathya, R, Mahesh, T R, Bhatia Khan, Surbhi, Malibari, Areej A, Asiri, Fatima, Rehman, Attique Ur, Malwi, Wajdan Al
• Format: Article
• Language: English
• Subjects: brain tumor classification; convolutional neural networks (CNN); deep learning; medical imaging; Medicine; transfer learning; Xception architecture
• ISSN: 2296-858X; 2296-858X
• DOI: 10.3389/fmed.2024.1487713

The classification of brain tumors from medical imaging is pivotal for accurate medical diagnosis but remains challenging due to the intricate morphologies of tumors and the precision required. Existing methodologies, including manual MRI evaluations and computer-assisted systems, primarily utilize conventional machine learning and pre-trained deep learning models. These systems often suffer from overfitting due to modest medical imaging datasets and exhibit limited generalizability on unseen data, alongside substantial computational demands that hinder real-time application. To enhance diagnostic accuracy and reliability, this research introduces an advanced model utilizing the Xception architecture, enriched with additional batch normalization and dropout layers to mitigate overfitting. This model is further refined by leveraging large-scale data through transfer learning and employing a customized dense layer setup tailored to effectively distinguish between meningioma, glioma, and pituitary tumor categories. This hybrid method not only capitalizes on the strengths of pre-trained network features but also adapts specific training to a targeted dataset, thereby improving the generalization capacity of the model across different imaging conditions. Demonstrating an important improvement in diagnostic performance, the proposed model achieves a classification accuracy of 98.039% on the test dataset, with precision and recall rates above 96% for all categories. These results underscore the possibility of the model as a reliable diagnostic tool in clinical settings, significantly surpassing existing diagnostic protocols for brain tumors.