A Hybrid Artificial Intelligence Model for Skin Cancer Diagnosis

Abstract

Melanoma or skin cancer is the most dangerous and deadliest disease. As the incidence and mortality rate of skin cancer increases worldwide, an automated skin cancer detection/classification system is required for early detection and prevention of skin cancer. In this study, a Hybrid Artificial Intelligence Model (HAIM) is designed for skin cancer classification. It uses diverse multi-directional representation systems for feature extraction and an efficient Exponentially Weighted and Heaped Multi-Layer Perceptron (EWHMLP) for the classification. Though the wavelet transform is a powerful tool for signal and image processing, it is unable to detect the intermediate dimensional structures of a medical image. Thus the proposed HAIM uses Curvelet (CurT), Contourlet (ConT) and Shearlet (SheT) transforms as feature extraction techniques. Though MLP is very flexible and well suitable for the classification problem, the learning of weights is a challenging task. Also, the optimization process does not converge, and the model may not be stable. To overcome these drawbacks, EWHMLP is developed. Results show that the combined qualities of each transform in a hybrid approach provides an accuracy of 98.33% in a multi-class approach on PH 2 database.

Transfer Learning Based Weighted Deep Learning Ensemble Model for Medical Image Classification

“…Natha et al [11] incorporated Contourlet Transform (CT) and Local Binary Pattern (LBP) techniques to identify skin cancer image features accurately. To handle computational costs and overfitting, they employed Particle Swarm Optimization (PSO) to select key features and used a Support Vector Machine (SVM), Random Forest (RF), and Neural Networks (NN) for classification.…”