Expert System for Diagnosing Skin Disease
Sherine Glory J1, Bhavani.M2, Baarath S3, Adarsh N4, Ajay Durai V5
1Ms. Sherine Glory J*, Department of Computer Science & Engineering, Rajalakshmi Engineering College, Chennai, (Tamil Nadu), India.
2Ms. Bhavani, Department of Computer Science & Engineering, Rajalakshmi Engineering College, Chennai, (Tamil Nadu), India.
3Baarath S, Department of Computer Science & Engineering, Rajalakshmi Engineering College, Chennai, (Tamil Nadu), India.
4Adarsh N, Department of Computer Science & Engineering, Rajalakshmi Engineering College, Chennai, (Tamil Nadu), India.
5Ajay Durai V, Department of Computer Science & Engineering, Rajalakshmi Engineering College, Chennai, (Tamil Nadu), India.
Manuscript received on May 01, 2020. | Revised Manuscript received on May 10, 2020. | Manuscript published on June 10, 2020. | PP: 9-12 | Volume-9 Issue-8, June 2020. | Retrieval Number: 100.1/ijitee.G5862059720 | DOI: 10.35940/ijitee.G5862.069820
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Abstract: The most unpredictable and difficult terrain in the field of medical diagnosis is dermatology. Dermatological diseases are the most prevalent diseases that one out of three men suffer skin disorder. Regardless of being prevalent , diagnosis of these diseases require more experience in domain. About 90 percent of skin disorders can be cured by primary care. This conveys that the early care for the disease is necessary . This early stage detection can be made easier by computer aided diagnosis system. Diagnostic expert-based computer systems that simulate the diagnostic ability of human body and disease. So we propose Expert system which classify skin diseases based on their appearance and its characteristics. Rather than training every diseases in single image classifier model . We categorize skin disease based on their characteristic and train model separately for each category. This system will filter and cleans data and categorize based on their characteristics. Feature extraction and classification using complex methods such as the convolutional neural network(CNN) and softmax classifier. This system will provide more accuracy, fast and efficient result than traditional method.
Keywords: Dermatology, Computer aided diagnosis, Expert System, Convolutional Neural Network.
Scope of the Article: Convolutional Neural Network