IRFNet: Skin Lesion Detection and Classification Using Unified Intuitive and Object Classifier with Iterative Random Forest Algorithm

Abstract

Skin cancer is one of the critical diseases that occurs in different age groups of the population. Early detection and prevention can save many lives. Melanoma is one of the fundamental skin cancer types. The traditional approaches for skin lesion detection and classification (SLDC) failed to distinguish between melanoma and non-melanoma skin malignancies, resulting in low classification accuracy. Thus, this article is focused on the implementation of computer aided detection method for SLDC using iterative random forest (IRF) algorithm, hereafter named as IRFNet. The proposed IRFNet acts as a decision-making tool for the dermatologists. Initially, the first level consists of five intuitive classifiers (ICs) named as perceptron coupled with color local binary patterns (PC-CLBP), histograms of oriented gradients (HOGs), the generative adversarial network coupled with ABCD rule (GAN-ABCD), ResNet, and AlexNet are used to extract the texture, shape, color, size, and convolutional pixel connections-based features from given skin lesion images. Then, IRF algorithm is used to select the optimal features and finally the object classifier called deep learning convolutional neural network (DLCNN) is employed to classify the type of lesion as either melanoma or non-melanoma based on back-propagation perceptron that provides the final decision. The extensive simulation outcome performed on ISIC-2019 and PH2 datasets discloses the superiority of the proposed IRFNet in terms of precision, recall, sensitivity, specificity, and f1-score. In addition, it also achieved the trade-off between classification accuracy and execution time as compared to the existing SLDC approaches.