Published Paper Details:

Skin infection diseases due to hazardous chemicals are more usual as compared to other common skin diseases in our society. The human beings encounter with diseases due to hazardous chemicals because of distinct environmental aspect present in the world and absence of individual care or other factors such as chemicals present in daily use products. The experience and proficiency of the doctor/pathologist determine accurate diagnosis of skin diseases, as inaccurate diagnosis leads to financial damage and adverse health effects on the patient. In our proposed research work, we will deal with the identification of skin diseases due to hazardous chemicals present in daily use product/chemicals such as pesticides, cosmetics, detergent, paint, sanitizer, petrol, diesel etc with machine learning algorithms. Convolution neural network is main approach of skin disease identification with efficient deep learning network model having best accuracy rate. Our hazardous chemicals skin disease identification model will utilized large number of images for training the network using ISIC dataset. The AlexNet, ResNet, VGGNet, Inception, DenseNet, MobileNet, Googlenet or any other efficient network will be used to conclude our proposed research work.