Medical Diagnostics Using Machine Learning-Nail Images

In this study, we explore a novel application of machine learning to revolutionize the field of medical diagnostics. This paper presents an innovative approach to automating the detection and classification of nail diseases using convolutional neural networks (CNNs) and machine learning techniques. By employing an extensive dataset of nail images representing various dermatological conditions, the system demonstrates a high accuracy rate of 92%. Through advanced image pre-processing methods and cutting-edge CNN architectures, we improve diagnostic precision while minimizing the variability that are typically seen in traditional clinical evaluations. The system facilitates faster and more reliable diagnoses, which can significantly enhance patient outcomes and broaden access to dermatological care by addressing the shortcomings of conventional methods. Future research will focus on optimizing the model architecture and developing real-time applications to further elevate its performance and usability in clinical environments.