"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."