Design and development of skin disease detection application in humans using computer vision - Krisogonus Wiero Baba Kaju; Nizirwan Anwar; Agung Mulyo Widodo; Binastya Anggara Sekti (Jurnal Mantik, 2024)
Skin diseases are abnormal conditions affecting the outer layer of the human body. The prevalence of skin infections worldwide reaches 300 million cases per year, with Indonesia contributing significantly, mainly due to the tropical climate and dense population. Factors such as air temperature, environmental cleanliness, personal hygiene, and the lack of public knowledge about skin hygiene can trigger various types of skin diseases. Skin diseases are often considered trivial as they do not cause death, yet if not promptly and accurately addressed, they can lead to spreading and difficulties in treatment. Analyzing skin diseases requires a high level of knowledge, and accurate diagnosis often presents a challenge. The success of diagnosis heavily relies on the experience of doctors, with some limitations involving subjective assessments and variations among experts. This research aims to design an artificial intelligence (AI)-based application that can quickly and accurately diagnose various types of skin diseases in humans. The application utilizes Deep Learning technology, employing the MobileNet model in Computer Vision to identify skin disease types based on images provided by the user. The system development method used is the AI Project Cycle, encompassing stages such as problem scoping, data acquisition, data exploration, modeling, evaluation, and deployment. Model evaluation results demonstrate good performance with an accuracy rate of 96%, precision of 96%, an f1 score of 95%, and a recall of 95%. The resulting application not only provides diagnoses but also offers information about symptoms, causes, and methods of handling the identified skin diseases