Comparing Skin Cancer Diagnosis between manual, 4 classical, and 1 deep machine learning algorithms

Abstract

Skin cancer incidence has increased significantly with approximately 1.2 million melanoma cases diagnosed each year globally. An experienced dermatologist’s visual inspection has only been able to achieve a maximum accuracy of 78%. Recently, convolution neural networks algorithms have successfully outperformed human detection. The study aim was to compare the accuracy and computational time of one deep and four shallow machine learning classification algorithms and demonstrate their statistical superiority over human detection. One deep and four shallow learning algorithms were used to compare the detection accuracy and computational time. Using verified skin lesion images from the ISIC Database, the deep and shallow machine learning algorithms were trained, validated, classified and tested to detect a cancerous skin mole. The results showed that the accuracy of the deep algorithm (VGG-16- 98.86%) was superior to both the shallow algorithms (SVMs – 88.29%, decision trees-88.62%, logistic regression -88.26%, neural network -88.59%) as well as human detection (78.3%). The shallow algorithms were also superior to human detection. Deep Learning algorithms, specifically convolution neural networks, can reduce the false negatives and false positives during melanoma detection and could also be used for early detection of skin cancer at home, saving lives and significant costs.