Osteoarthritis is the most common form of “Arthritis & Joint disease”. Osteoarthritis (OA) is one of the fundamental causes of older and overweight individual’s sickness. It is the main cause of disability in adults. Mostly this disease occurs in people above 45 years of age, in which women suffer more as compared to men. it is basically damaged the Cartilage, because of which bones rub each other causing intense pain and inflammation. this gets thick and makes spurs at the edges. The knee Osteoarthritis is of 4 grades according to X-ray. The first 2 grade and 3rd grade can be recovered with the help of therapy and medications, while the 4th grade is necessary for knee replacement. The emerging Osteoarthritis management approach involves clinical evaluation & diagnostic imaging techniques. Within this research, we explore descriptively and objectively the various medical imaging methods used to diagnose and identify knee osteoarthritis. We study on the automatically detection of recovery rate of human disease and classify Osteoarthritis in the knee from medical images (like Magnetic Resonance image, CT scan, X-ray) from various medical image classification procedures. This paper provides a study that focuses on the various medical imaging methods used to determine osteoarthritis.

Alzheimer's disease is a neurodegenerative brain disease that kills neurons. The global prevalence of the disease is gradually growing. In all leading countries, it is one of the senior citizens' leading causes of death. So, much research shows that early detection of illness is the most critical factor in improving patient care and treatment outcomes. Currently, AD is diagnosed by the manual study of magnetic resonance imaging, biomarker tests, and cognitive tests. Machine learning algorithms are used for automatic diagnosis. However, they have certain limits in terms of accuracy. Another issue is that models trained on class-unbalanced datasets often have poor results. Therefore, the main objective of the proposed work is to include a pre-processing method before the hybrid model to improve classification accuracy. This research presents a hybrid model based on a deep learning approach to detect Alzheimer’s disease. Which, we are using the SMOTE method to equally distribute the classes to prevent the issue of class imbalance. The hybrid model uses Inception V3 and Resnet50 to detect characteristics of Alzheimer's disease from magnetic resonance imaging. Finally, a dense layer of convolution neural network is used for classification. The hybrid approach achieves 99% accuracy in classifying MRI datasets, which is better than the old work. These results are better than existing approaches based on accuracy, specificity, sensitivity, and other characteristics.