A Transfer Learning Approach for Classification of Knee Osteoarthritis

Abstract

Artificial intelligence is a concept that is extremely popular in the realm of healthcare and medical imaging. It strives to generate experimental findings that are beyond the capacity of humans and encourages consistent outcomes in assisting clinical specialists. Doctors that rely substantially on pictures, such as radiographers profit greatly from medical X-ray image analysis. Early-stage Knee Osteoarthritis detection is one such imaging prognosis. Wear and tear along with the slow degeneration of the articular cartilage are the main causes of knee osteoarthritis. Due to sophisticated technology, osteoarthritis detection employing X-ray pictures demands professionals who are technically proficient. Long examination periods and erroneous outcomes might stem from a lack of professional expertise. Thus, in this paper, a rapid and effective technique of utilizing Artificial Intelligence, medical image processing, and Machine Learning, has been suggested, to aid clinicians in making proper conclusions in classifying Knee Osteoarthritis at its early stages. The intricacies of Artificial Intelligence will surely aid in the faster adoption of technology in healthcare.