Paper #3

Mobile-based Skin Lesions Classification Using Convolution Neural Network

Nazia Hameed, Antesar Shabut, Fozia Hameed, Silvia Cirstea, Sorrel Harriet and Alamgir Hossain

Abstract: This research work is aimed at investing skin lesions classification problem using Convolution Neural Network (CNN) using cloud-server architecture. Using the cloud services and CNN, a real-time mobile-enabled skin lesions classification expert system “i-Rash” is proposed and developed. i-Rash aimed at early diagnosis of acne, eczema and psoriasis at remote locations. The classification model used in the “i-Rash” is developed using the CNN model “SqueezeNet”. The transfer learning approach is used for training the classification model and model is trained and tested on 1856 images. The benefit of using SqueezeNet results in a limited size of the trained model i.e. only 3 MB. For classifying new image, cloud-based architecture is used, and the trained model is deployed on a server. A new image is classified in fractions of seconds with overall accuracy, sensitivity and specificity of 97.21%, 94.42% and 98.14% respectively. i-Rash can serve in initial classification of skin lesions, hence, can play a very important role early classification of skin lesions for people living in remote areas.

Keywords: Skin lesions classification; mobile-enabled skin lesion classification; convolution neural network acne classification; eczema classification; psoriasis classification; deep learning; SqueezeNet.