Enhancing Medical Diagnosis with Deep Learning: Automated Detection of Alzheimer's and Skin Diseases

Alzheimer's disease, a common form of dementia, progressively impairs cognitive abilities, leading to dependency on assistance. Traditional detection methods like MRI scans are often cumbersome, while deep learning approaches enable faster, more accurate early diagnosis. Using an imbalanced dataset from Kaggle, techniques like SMOTE balanced the data, and a convolutional neural network model classified Alzheimer's severity—very mild, mild, moderate, and severe—achieving superior precision and accuracy. Similarly, skin diseases, posing risks like cancer and mental health issues, require automated diagnosis due to the subjective nature of manual assessment. This study employed digital hair removal, noise reduction via Denoising CNN, lesion segmentation using U-Net, and classification is done by CNN. Both studies underscore the role of machine learning in enhancing medical diagnosis and treatment.