Efficient Net Based Brain Encephalopathy using Data Augmentation Techniques for Alzheimer and Peripheral Pathological Outcomes

Detecting Alzheimer in human brain MRI scans is a crucial yet tough task that carries significant implications for clinical practice. This study delves into using deep learning algorithms, particularly EfficientNet B5, to accurately and automatically spot Alzheimer. By tapping into the capabilities of image processing and convolutional neural networks, our approach aims to create a strong and efficient solution. We work with a diverse set of MRI scans, covering different Alzheimer stages and types, to train and test the model's effectiveness. The main contributions of our research involve building a thorough process for preparing MRI data, fine-tuning the EfficientNet B5 structure and conducting a detailed analysis of how well the model performs. Our results showcase promising levels of accuracy, sensitivity and specificity in categorizing Alzheimer, highlighting the potential of deep learning algorithms to aid clinicians in swiftly and precisely diagnosing Alzheimer. This could significantly bolster patient care and treatment strategies.