Skin Diseases Diagnosis Using Convolutional Neural Network

Abstract

Skin is the most powerful protection of
important organs in the human body. It acts as a shield
to protect our internal body to get damaged. But this
important part of the human body can be affected by so
serious infections caused by some fungus or viruses or
even dust too. Around the world, millions of people
suffer from various skin diseases. From acne problems
to eczema people suffer a lot. Sometimes a small boil on
the skin can turn into a severe issue or even an
infection that will cause a major health issue. Some
skin issues are so contagious that one can be affected
by another just with a handshake or using a
handkerchief. A proper diagnosis can result in proper
medication that can reduce the miseries of the people
suffering create awareness. In this project we are using
CNN (convolution neural networks) to classify skin
diseases from images as CNN gain lots of success and
popularity in the field of image classification. To train
CNN we have used skin disease dataset which contains
9 different types of diseases such as 'Actinic Keratosis',
'Basal Cell Carcinoma', 'Dermatofibroma',
'Melanoma', 'Nevus', 'Pigmented Benign Keratosis',
'Seborrheic Keratosis', 'Squamous Cell Carcinoma'
and 'Vascular Lesion'. After training CNN algorithm
we can upload any test image then CNN will detect and
classify disease from that image