Affiliation:
1. Faculty of Automatic Control and Computers, University Politehnica of Bucharest, 060042 Bucharest, Romania
2. “Ștefan S. Nicolau” Institute of Virology, 030304 Bucharest, Romania
Abstract
Today, skin cancer, and especially melanoma, is an increasing and dangerous health disease. The high mortality rate of some types of skin cancers needs to be detected in the early stages and treated urgently. The use of neural network ensembles for the detection of objects of interest in images has gained more and more interest due to the increased performance of the results. In this sense, this paper proposes two ensembles of neural networks, based on the fusion of the decisions of the component neural networks for the detection of four skin lesions (basal cancer cell, melanoma, benign keratosis, and melanocytic nevi). The first system is based on separate learning of three neural networks (MobileNet V2, DenseNet 169, and EfficientNet B2), with multiple weights for the four classes of lesions and weighted overall prediction. The second system is made up of six binary models (one for each pair of classes) for each network; the fusion and prediction are conducted by weighted summation per class and per model. In total, 18 such binary models will be considered. The 91.04% global accuracy of this set of binary models is superior to the first system (89.62%). Separately, only for the binary classifications within the system was the individual accuracy better. The individual F1 score for each class and the global system varied from 81.36% to 94.17%. Finally, a critical comparison is made with similar works from the literature.
Reference31 articles.
1. Skin cancer detection using non-invasive techniques;Narayanamurthy;RSC Adv.,2018
2. (2021, May 21). Available online: https://www.skincancer.org/skin-cancer-information/skin-cancer-facts/.
3. Popescu, D., El-Khatib, M., El-Khatib, H., and Ichim, L. (2022). New Trends in Melanoma Detection Using Neural Networks: A Systematic Review. Sensors, 22.
4. A Review of Noninvasive Techniques for Skin Cancer Detection in Dermatology;Heibel;Am. J. Clin. Dermatol.,2020
5. Hair Segmentation and Removal in Dermoscopic Images Using Deep Learning;Bibiloni;IEEE Access,2021