Study and Performance Evaluation of Brain MRI Images Using Aartificial Intelligence

The limits and potential of medical imaging are expanded by artificial intelligence. Therefore, in an effort to improve the performance and accuracy of diagnosing brain abnormalities, researchers are constantly working to create an effective and automated diagnosis method. Tumour identification and diagnosis have been achieved by the use of magnetic resonance imaging (MRI). Medical professionals can identify and categorise tumours as normal or abnormal with the aid of digital image processing. This research focuses on various neural networks for brain MRI tumour and non-tumour image categorization and confusion matrix performance evaluation. Otsu's thresholding approach is used for segmentation out of all the segmentation techniques. For feature extraction, a grey level co-occurrence matrix (GLCM) is employed. The classification techniques utilized in this study produce the necessary results in terms of confusion matrix parameters, which may be used to assess the classifier's performance in terms of F1 score, accuracy, sensitivity, and precision.