Leveraging Transfer Learning and Image Processing for Brain Tumor Classification from MRI Data

This paper presents a deep learning approach for the classification of brain tumors using MRI images, leveraging the Xception architecture for efficient and accurate detection. The dataset consisted of images related to brain tumors and healthy ones. It was preprocessed and image enhancement was done on it to improve the performance of the model. The network that was fine-tuned with transfer learning resulted in a test accuracy of about 0.989 with minor loss, indicating its good discriminative capability between the case of brain tumor and the healthy one. Evaluation of the performance by confusion matrix analysis indicates high sensitivity and specificity levels with just minor false positives and false negatives. The proposed model shows tremendous potential for real-world medical applications as it offers an automated accurate, and efficient capability of detecting the existence of brain tumors. This work provides a basis for further improvements that come in exploring different types of datasets as well as using 3D MRI scans to further improve the strength of the system. It is shown from these results that deep learning is viable as the next frontier toward improved brain tumor diagnosis: hence, more promise by this model, in early detection and subsequent reduction of workloads at diagnostic levels within clinical practice.