Dilshan Nandasena

Work place: Axiata Digital Labs PVT LTD, Colombo, Sri Lanka

E-mail: dilshan.nandasena@axiatadigitallabs.com


Research Interests: Image Processing, Image Manipulation, Image Compression, Computational Learning Theory


Dilshan Nandasena is currently a Software Engineer at Axiata Digital Labs, Sri Lanka. He received BSc. In Software Engineering degree from the General Sir John Kotelawala Defence University, Sri Lanka. His current research interests include Machine Learning and Image processing.

Author Articles
Mobile-Based Skin Disease Diagnosis System Using Convolutional Neural Networks (CNN)

By M.W.P Maduranga Dilshan Nandasena

DOI: https://doi.org/10.5815/ijigsp.2022.03.05, Pub. Date: 8 Jun. 2022

This paper presents a design and development of an Artificial Intelligence (AI) based mobile application to detect the type of skin disease. Skin diseases are a serious hazard to everyone throughout the world. However, it is difficult to make accurate skin diseases diagnosis. In this work, Deep learning algorithms Convolution Neural Networks (CNN) is proposed to classify skin diseases on the HAM10000 dataset. An extensive review of research articles on object identification methods and a comparison of their relative qualities were given to find a method that would work well for detecting skin diseases. The CNN-based technique was recognized as the best method for identifying skin diseases. A mobile application, on the other hand, is built for quick and accurate action. By looking at an image of the afflicted area at the beginning of a skin illness, it assists patients and dermatologists in determining the kind of disease present. Its resilience in detecting the impacted region considerably faster with nearly 2x fewer computations than the standard MobileNet model results in low computing efforts. This study revealed that MobileNet with transfer learning yielding an accuracy of about 85% is the most suitable model for automatic skin disease identification. According to these findings, the suggested approach can assist general practitioners in quickly and accurately diagnosing skin diseases using the smart phone.

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