Paper Title
HELPING SYSTEM FOR BRAIN DISEASE DIAGNOSIS USING DEEP LEARNING

Abstract
Abstract - Artificial intelligence (AI) and Biomedical data analysis have advanced significantly in recent years, providing new opportunities for disease diagnosis, treatment planning, and patient monitoring. Deep transfer learning, which is part of AI, extracts efficiently features and patterns from biomedical images leading to a precise disease characterization and diagnosis by revealing hidden structures. The present work provides a comprehensive review of cutting-edge methods for analyzing biomedical data related to brain tumors and Alzheimer’s disease. The study emphasizes the importance of deep transfer learning models such as ResNet50 and DenseNet-121 as well as the role of data augmentation techniques in enhancing their effectiveness. The results obtained showed that the techniques used are very effective in providing very satisfactory performances in terms of accuracy and precision. Keywords - Deep Transfer Learning, Biomedical Data, Disease Diagnosis, Data Augmentation, CNN Parameter Optimization.