Deep Learning with Multi-Modal Imaging: An Advanced Approach to Brain Tumor Detection

Brain tumors represents one of the major global health issue, making it crucial to develop advanced and precise detection methods for early diagnosis and effective treatment planning. This research endeavors to advance the field of Brain Tumor detection by integrating deep learning techniques with multi-modal imaging, offering a comprehensive approach that capitalizes on the strengths of various imaging modalities. Convolutional Neural Networks (CNNs) have proven to be a promising and powerful tool for automating the detection of brain tumors in magnetic resonance imaging (MRI) scans. Compared to early machine learning models such as Support Vector Machines (SVMs), CNNs offer significant advantages. Random Forests, and k-Nearest Neighbors (KNN) have also been employed for Tumor detection in this context. However, the limitations posed by traditional algorithms like reliance on handcrafted features, limited adaptability to diverse data distributions, and challenges in capturing complex spatial patterns furnishes the necessity for advanced algorithms. The advanced CNNs have revolutionized Brain Tumor detection in medical imaging by automating the process of feature extraction, preserving spatial hierarchies, and enabling accurate and efficient Tumor identification. The different CNN architecture such as CNN with Uncropped lesion, CNN with segmented lesion, CNN with cropped lesion, Deep CNN, 2 Dimensional CNN is examined for detecting tumor. The performance of distinct CNN architectures is compared with traditional methodologies in terms of accuracy, precision and recall. The CNN with uncropped lesion furnishes accuracy of 99% which is around 10% higher than traditional Brain Tumor detection methods.