Enhanced Detection of Brain Tumors and Neurological Conditions Using Convolutional Neural Networks: A Comprehensive Study on the Xception Model

Medical imaging is a very good area of investigation that has led to a lot of problems that calls for solving. Convolutional Neural Networks (CNN) is one of the most efficient methods used for classification task of brain MRI scans with the emphasis being on the model Xception. The research seeks to achieve classification of MRI text into two groups which are normal and abnormal. A variety of methodological improvements including but not limited to, data augmentation, learning rate scheduling and tuning of models were carried out in order to achieve optimization. An evaluation of the model is also carried out including its accuracy and loss functions as well as using the ROC curves and precision recall curves looking at its diagnosis performance. The outcomes indicate the promise that the CNN approach offers in medical images, apart from the limitations faced such as that of overfitting and generalizability. Future directions for research are discussed as well, which include improving specificity in classification as well as extending the classification task to that of discerning different diseases in detail.