Leveraging CNNs for Automated Dermatological Diagnosis: Advancements in Skin Disease Prediction

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Abstract: This is a research paper focused on skin disease prediction using CNN algorithms. Standing on eight classes of data this study accepts the challenge of proper diagnosis of multiple skin diseases. This problem statement supports the call for better and accurate ways of sorting the skin diseases so that dermatologists can diagnose the diseases appropriately and within the right time. With respect to the method, most of the data pre-processing are done on the collected dataset to improve its quality so as to develop a CNN model. Next, testing is performed with using the architecture of a CNN that is trained to search for features and patterns of the skin diseases’ photos. In this study, accuracy rates for each of the eight disease categories reach high figures, according to the results: so, it can be seen that the attainment of the CNN models is quite remarkable to classify diseases. For instance, the suggested method, stands as a fair one because it does not very much depend on the quality of the input image. The paper also examines the possibility of interpreting the CNN models concerning what kind of image features and area contribute to the classification process. This feature makes the model prediction more credible and yields useful information on the way the decision is made, possibly making doctors understand various aspects of pathophysiologic processes associated with different skin ailments. Furthermore, the study seeks to investigate the possibility of the proposed method in proximate actual clinical applications with identification always fast, and with massive data amount. In addition, solving these problems also contributes to the development of not only the dermatology as a branch of medicine, but also improves the understanding of the applicability of CNN based approaches for the analysis of medical images.