Osteoarthritis is a degenerative joint sickness that influences a huge number of individuals around the world. Early location and determination of osteoarthritis is basic for viable treatment and the board of the illness. As of late, warm imaging has arisen as a promising painless procedure for identifying osteoarthritis. To address these difficulties, we propose an original methodology for osteoarthritis recognition in warm pictures utilizing the half and half ResNet-SqueezeNet model profound learning design. The proposed approach includes pre-handling the warm pictures to improve their highlights, trailed by division to extricate the area of interest. The segmented region is then fed into the hybrid ResNet-SqueezeNet model, which is trained to classify the thermal image as normal or abnormal based on the presence of osteoarthritis. The performance of the proposed approach is evaluated on a dataset of thermal images collected from patients with osteoarthritis and healthy controls.
