A Hybrid Machine Learning and Deep Learning Approach for Brain Tumor Segmentation and Disease Type Prediction

The early detection and precise classification of brain tumours are critical in medical imaging, as timely intervention directly impacts the selection of optimal treatment strategies for patients. Accurate tumour segmentation, particularly in brain imaging, plays a vital role in identifying and delineating abnormal tissue regions to support clinical decision-making. Advances in machine learning and deep learning have significantly enhanced the diagnostic accuracy of medical imaging by enabling precise identification and characterization of tumours. In this study, a hybrid framework is proposed for brain tumour segmentation and disease classification, utilizing magnetic resonance imaging (MRI) data. The deep learning model VGG16 was employed to extract high-dimensional features from MRI scans, followed by the application of three machine learning algorithms-Support Vector Classifier (SVC), decision tree, and K-means clustering-for classification tasks. The hybrid approach demonstrated superior performance, with the Kmeans algorithm achieving the highest segmentation accuracy of 97%. The SVC model achieved an accuracy of 91%, while the decision tree algorithm yielded an accuracy of 89%. This framework offers a robust solution for both tumour segmentation and classification, with potential clinical applications for automating the diagnosis of brain tumours. Notably, the model distinguishes between four tumour types, including three malignant variants, contributing to more effective treatment planning. The integration of deep learning and machine learning techniques within this framework underscores the potential for improving diagnostic efficiency and accuracy in brain tumour analysis.