AI Driven Innovation in early Detection and Diagnosis of Brain Cancer

Abstract

The continuous advancement of artificial intelligence (AI) has brought about a significant transformation in the healthcare sector, namely in the domain of early identification and diagnosis of intricate medical ailments. The present study investigates the use of artificial intelligence (AI) in the identification and diagnosis of brain tumors at an early stage. This research capitalizes on a combination of advanced methodologies such as Genetic Algorithms, Local Binary Patterns (LBP), Deep Learning-Based Segmentation, and Support Vector Machines (SVM) to achieve its objectives. Genetic algorithms are utilized in the context of feature selection to optimize the discriminative capability of input data. The use of Local Binary Patterns (LBP) presents a reliable method for doing texture analysis, hence improving the characterization of diseased areas in brain imaging. Deep learning-based segmentation approaches have demonstrated high efficiency in extracting tumor boundaries and accurately distinguishing them from healthy brain tissue, hence enabling precise localization. The Support Vector Machine (SVM) technique, which is highly effective in classification tasks, plays a crucial role in the diagnostic process by accurately distinguishing between benign and malignant tumour cases. The utilisation of an interdisciplinary method not only enhances the precision and dependability of brain tumor diagnosis, but also accelerates the procedure, therefore facilitating prompt intervention and potentially life-preserving therapeutic alternatives for individuals. The research highlights the significant capacity of AI-based approaches in revolutionizing the field of neuroimaging, emphasizing their crucial contribution to augmenting the abilities of healthcare practitioners in the essential undertaking of identifying and diagnosing brain tumors.