Vaibhav Mali

Data Science Skills

Artificial Intelligence	Statistical Modeling	Text Analytics
Big Data	Digital Analytics	Visualization
Python	R	Tableau

Domain Skills

Banking and Finance	Health Care	Telecommunications
Retail	Insurance

23 Skin Disease Image Classification Using CNN

Category: Domain Usecases

Sub-Category: Health Care and Pharmaceuticals

Use-case Type: Image Classification

Tags: convolutional_neural_network,health_analysis,healthcare,image_classification,skin_disease

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Use Case Summary

Skin diseases are more common than other diseases. Skin diseases may be caused by fungal infection, bacteria, allergy, or viruses, etc. The advancement of lasers and Photonics based medical technology has made it possible to diagnose the skin diseases much more quickly and accurately. But the cost of such diagnosis is still limited and very expensive. So, image processing techniques help to build automated screening system for dermatology at an initial stage. The extraction of features plays a key role in helping to classify skin diseases.

We proposed an image processing-based method to detect skin diseases. This method takes the digital image of disease effect skin area, then use image analysis to identify the type of disease.

Description Of Dataset:

The data consists of images of 23 types of skin diseases.The total number of images are around 19,500, out of which approximately 15,500 have been split in the training set and the remaining in the test set.

Source Of Dataset:

https://www.kaggle.com/datasets/shubhamgoel27/dermnet

Training Images:15,500

Testing Images:4000

Model: CNN

Accuracy: 98.00 %