DEEP LEARNING ON MEDICAL IMAGE PROCESSING: BRAIN TUMOR PREDICTION USING TABULAR AND VISUAL DATA INTEGRATION

Abstract

Deep learning (DL) techniques have created an unparalleled revolution in medical image processing, speeding up, improving, and making diagnosis of disease more understandable. This paper will discuss how deep neural networks (DNNs) can help to identify brain tumours using a large clinical dataset which includes demographic, medical, and diagnostic factors. The model demonstrated perfect training and validation accuracy after optimization, and it represents the enormous potential of DL in medical data analytics with also pointing out the need to address the risks of overfitting. This paper discusses the importance of data structure, interpretability, and computational efficiency of medical deep learning with a thorough discussion of the data preprocessing process, relationships between features, and selection of training strategies. The model enables the visualization of the behavior and insights of the model using six key figures such as correlation matrices, feature importance charts, and learning curves. The paper ends with directions to come such as federated AI, hybrid models, as well as viability of interpretability to make AI usable in clinical settings.