DEEP LEARNING APPLICATIONS IN MEDICAL IMAGE ANALYSISBRAIN TUMOR

Abstract

Brain tumors are among the most critical neurological disorders, requiring early and accurate diagnosis to improve patient survival rates and treatment outcomes. Medical imaging techniques such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Positron Emission Tomography (PET) play a vital role in detecting and analyzing brain tumors. However, manual interpretation of medical images is time-consuming and highly dependent on the expertise of radiologists. Recent advancements in deep learning have significantly enhanced the automation and accuracy of medical image analysis. This paper explores deep learning applications in brain tumor analysis using medical imaging data. The proposed framework employs deep neural networks, particularly Convolutional Neural Networks (CNNs), for image preprocessing, feature extraction, tumor segmentation, classification, and detection. Deep learning models automatically learn complex patterns from medical images, enabling precise identification of tumor regions and differentiation between tumor types. Experimental analysis demonstrates that deep learning-based approaches achieve superior accuracy, sensitivity, and reliability compared to traditional image analysis methods. The proposed framework provides an intelligent and efficient solution for supporting radiologists in brain tumor diagnosis and clinical decision-making.