Detecting Knee Osteoarthritis Using CNN Enhanced With Alex Net

This research proposes a new technique for the early diagnosis of Knee Osteoarthritis (KOA) based on the latest generations of Convolutional Neural Networks in combination with the latest architectures, namely AlexNet, MobileNet, and ResNet. The project explores the analysis of medical imaging data to boost the accuracy and efficiency in the diagnosis process through X-ray or MRI scans. The incorporation of these powerful models enables the system to detect the early stages of osteoarthritis with higher accuracy, enabling better and timely treatment. Image recognition can be thought of as a strong base for AlexNet, computationally efficient for Mobile Net, mainly for mobile health applications, and skip connections in ResNet enhance the depth and performance of the model by reducing the problem of vanishing gradients. This approach will have the effect of strengthening the diagnosis while greatly reducing the number of false positives and false negatives, thereby reducing the burden on the healthcare provider. This system is also scalable and can be applied to a wide range of different imaging modalities and medical conditions. By tapping into deep learning power, this technique is all set to transform the ability to detect early knee osteoarthritis, with benefits in improved patient outcome and expedited delivery of care. It serves as an essential tool for clinicians, recommending a proactive approach that espouses preventive care and individualized treatment planning.