Multiclass skin cancer classification using particle swarm optimization and convolutional neural network with information security

Abstract

Skin cancer is one of the world’s most common cancers. Among different skin cancer types, melanoma is very dangerous. Its treatment is possible if detected at an early stage. Detection of skin cancer is still challenging, even for experienced dermatologists, due to the high similarity between benign and malignant lesions. Deep learning technologies help in diagnosing skin lesions and classifying them. They are used to detect lesions at an early stage. However, it still struggles with complex skin lesions because of complicated properties such as fuzzy boundaries, artifacts, low background contrast, and insufficient training datasets. Medical images require information security also. This paper proposes a method using particle swarm optimization (PSO) with the ensemble of pretrained convolutional neural network PSOCNN architecture for skin disease classification with information security. To evaluate the proposed architecture, 10,015 samples of the publicly available HAM1000 (human against machine) dataset comprise seven distinct classes. It is the case of a multiclass skin lesion classification problem. This method achieves 97.82% accuracy and a macroaverage ROC curve area of 0.99. The average F1 score obtained is 0.98. This work has been compared with different studies present in the literature. The result shows that this work has achieved comparable classification accuracy. It helps in achieving better performance for all classes of multi-class classification.