Application of Multilevel Thresholding and CNN for the Diagnosis of Skin Cancer Utilizing a Multi-Agent Fuzzy Buzzard Algorithm

Abstract

This paper presents a novel methodology for skin cancer diagnosis based on deep learning and metaheuristics. The method combines a multi-level optimal thresholding segmentation technique based on the histogram of the input images with a new improved metaheuristic algorithm, based on Multi-agent Fuzzy Buzzard Algorithm (MAFBUZO) for this purpose. The MAFBUZO combines local search agents in multi-agent systems with the global search ability of the BUZO algorithm, enabling an appropriate balance of exploitation and exploration steps during optimization. To show the algorithm efficiency, it has been validated with four benchmark functions and its comparison with some other bio-inspired methods shows that using the proposed MAFBUZO, with 16.3459–16, 9.6538, 2.7412, and7.3258e-5 error provides the minimum error values during the minimization which shows its effectiveness to use in our application. After the region of interest segmentation, the images have been injected into an optimum convolutional neural network based on MAFBUZO for feature extraction and the final diagnosis. The technique is evaluated against Dermquest and DermIS datasets, and the obtained results are compared against those provided by other published techniques. The results show that using the proposed method with 0.95, 0.88, 0.94, and 0.93 values for NPV, PPV, Accuracy, and Specificity, provides the best outcomes against the other comparative approaches. The final results show that this method improves the results of the existing techniques in terms of overcoming weaknesses such as being trapped in local optimal points or having raw convergence issues.