Multiple Skin-Disease Classification Based on Machine Vision Using Transfer Learning Approach

Covering the majority of our body parts, the outer shell-like structure that protects the human body from any outcoming harms, is perhaps the skin. Being the most exposed part, it also suffers from different infectious diseases that causes the inside organs to be vulnerable too. Although it is pretty common to be affected by several skin diseases, identifying the disease flawlessly is often seen to be confusing as the diseases tend to be hard to distinguish between. Applying computer vision with a decent trained classification model can come in really useful in such scenarios. Among vastly available classification models, not every model can perform similarly in terms of identifying the precise disease category. To solve this concern, a custom collected dataset has been gathered, processed according to needs and afterwards, a transfer learning model known as "MobileNet-v2" has been trained and tested. The testing accuracy as demonstrated by the model was 83% in terms of both the testing dataset and unseen images. The study reflects that, if flawless dataset is ensured and the training parameters are maintained, accurate skin disease detection can be automated and at the same time it can be lightweight that reduces resource usages being a light model. The trained model can also be useful in medical implementation by taking further improving techniques.