Brain Tumor Detection Using Deep Learning
Dr. Prof. ML Sharma, Mayank Gahlot and Suraj Chaudhary
Maharaja Agrasen Institute of Technology, Rohini, Delhi, India
ABSTRACT
Brain tumor detection is a critical task in neuroimaging that significantly impacts clinical diagnosis and treatment planning. Conventional methods for tumor detection rely on manual interpretation of medical imaging data, which can be time-consuming and subject to interobserver variability. With the advancements in deep learning, particularly in the realm of convolutional neural networks (CNNs), there has been a growing interest in leveraging these techniques for automated brain tumor detection.
This study explores the efficacy of deep learning models in the automated detection and classification of brain tumors from magnetic resonance imaging (MRI) scans. A comprehensive dataset of MRI images representing various tumor types and anatomical regions is employed to train and evaluate the deep learning models. The developed CNN-based architectures are designed to capture intricate patterns and features within the MRI scans, facilitating accurate tumor localization and classification.
The research investigates the performance of the deep learning models in distinguishing between healthy brain tissue and different tumor subtypes, including gliomas, meningiomas, and metastatic tumors. Performance metrics such as accuracy, sensitivity, specificity, and area under the curve (AUC) are employed to assess the models' ability to precisely identify tumor regions and provide clinically relevant information to aid healthcare professionals.
Furthermore, the study addresses challenges associated with model generalization, interpretability, and scalability for deployment in clinical settings. Strategies for optimizing model robustness, enhancing interpretability, and ensuring seamless integration into existing diagnostic workflows are explored.
The findings of this research contribute to the growing body of knowledge regarding the application of deep learning in medical imaging analysis, particularly in the domain of brain tumor detection. The implications of this study extend to the development of more efficient and accurate tools to assist radiologists and clinicians in diagnosing brain tumors, thereby potentially improving patient outcomes and treatment strategies.
KEYWORDS
Brain Tumor, Deep Learning, Convolutional Neural Networks (CNN), Medical Imaging, Magnetic Resonance Imaging (MRI), Tumor Detection, Image Analysis, Neuroimaging, Machine Learning, Computer-Aided Diagnosis.
