Implementing Deep Learning Models for Accurate Brain Abnormality Detection and Positioning Using MRI and CT

Abstract

Medical imaging is mostly dependent on brain imbalance diagnosis as it enables physicians to locate and treat brain diseases early on. Conventional methods of diagnosis rely much on the views of professionals, who may take a long time and result in errors. Deep learning models have great potential to assist automate the categorisation of medical pictures, improve diagnosis accuracy, and lower the manual labour required in diagnosis process. However, modern models must be tuned to increase their accuracy as they often suffer with how they consume computer capability. Using MRI and CT pictures, this study uses deep learning models, especially ResNet50 and Xception, to sort and pinpoint brain problems. A number of classification models were made and tested to see how well they did in terms of accuracy, precision, and memory. These models included normal, lightweight, and fine-tuned versions of ResNet50 and Xception. The outcomes indicate that fine-tuned Xception did better than other models, with better localisation and classification accuracy. Also, combining MRI and CT scans was looked into as a way to improve model performance, which led to more consistent classification. A comparison of models shows that deep learning is good at automatically finding brain problems, which could lead to big steps forward in medical diagnosis. The study recognize optimised deep learning models make brain abnormality recognition much more accurate and reliable, cutting the need for human analysis. More study will be done in the future to improve model designs and add Explainability methods so that they can be used in clinical settings.