A Comprehensive Joint Learning System to Detect Skin Cancer

Abstract

Skin, the body's biggest organ and a barrier against heat, light, damage, and infection can be affected by many diseases. However, a correct diagnosis can lead to proper treatment. Skin diseases must be identified early to reduce skin lesion growth and spread. The medical field has a significant dependency on Information Technology and in this era, there is a need for a mechanism that can detect skin diseases at an early stage with higher accuracy capable of working with rapidly growing data. This research offers a joint learning system using Convolutional Neural Networks (CNN) and Local Binary Pattern (LBP) followed by its concatenation of all the extracted features through CNN and LBP architecture. The proposed system is trained and tested using the widely used publicly accessible dataset for skin cancer detection to solve multiclass skin disease issues. Furthermore, a comparison of results is developed between the architectures and their fusion. The demonstration of the results shows the robustness of the fusion architecture with an accuracy of 98.60% and a validation accuracy of 97.32%. Comparative results are also included in this research for better analysis.