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This work develops two neural-network ensemble approaches for four skin lesion categories: basal cell cancer, melanoma, benign keratosis, and melanocytic nevi. One system uses separate learning of MobileNet V2, DenseNet 169, and EfficientNet B2 with weighted multi-class prediction, while the other uses six binary models per network with weighted summation across classes and models. 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Detection of Malignant Skin Lesions Based on Decision Fusion of Ensembles of Neural Networks. Cancers 2023, 15, 4946. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)cancers15204946  \nAcademic Editor: David Wong  \nReceived: 1 September 2023  \nRevised: 2 October 2023  \nAccepted: 8 October 2023  \nPublished: 11 October 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Faculty of Automatic Control and Computers, University Politehnica of Bucharest,  \n060042 Bucharest, Romania; [loretta.ichim@upb.ro](loretta.ichim@upb.ro) (L.I.); [razvanionutm@yahoo.ro](razvanionutm@yahoo.ro) (R.-I.M.); [madalina.serghei27@gmail.com](madalina.serghei27@gmail.com) (M.-O.S.)  \n2 “S, tefan S. Nicolau” Institute of Virology, 030304 Bucharest, Romania  \n* Correspondence: [dan.popescu@upb.ro](dan.popescu@upb.ro); Tel.: +40-766218363  \nSimple Summary: Due to various causes, such as the thinning of the ozone layer, climate change, orthe fashion for artificial tanning, the incidence of skin cancer has increased recently. Early detection of cancerous skin lesions is the only chance to prevent dangerous developments. This paper proposes two models of support systems for the detection of some skin lesions including dangerous melanoma, based on two different methods of assembling neural networks and making global decisions by fusing individual decisions. The system based on the fusion of some intermediate binary classification subsystems is a new solution and offers an accuracy of 91.04%, better than that offered by the classic system based on the vote with multiple weights of the constituent networks. In addition, it is found that the individual performance indicators depend on the type of skin lesion. For example, the F1 score varied from 81 .36% to 94 . 17% .  \nAbstract: 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.  \nKeywords: skin lesion detection; image processing; neural networks; decision fusion; neural networks ensemble  \n1. Introduction  \nSkin cancer represents one of th","cbCaibQetVPwMSJO","https://ap.wps.com/l/cbCaibQetVPwMSJO","pdf",4009085,"English","# Simple Summary\n# Abstract\n# Keywords\n# 1. Introduction","[{\"question\":\"What is the main problem addressed in the study?\",\"answer\":\"The study targets early detection of malignant skin lesions, with emphasis on melanoma, to reduce dangerous outcomes through timely treatment.\"},{\"question\":\"How do the two proposed ensemble systems differ?\",\"answer\":\"The first system trains three neural networks (MobileNet V2, DenseNet 169, EfficientNet B2) and performs weighted overall prediction for four lesion classes. The second system builds six binary models for each network pair of classes and fuses results using weighted summation per class and per model.\"},{\"question\":\"What performance does the decision-fusion system achieve?\",\"answer\":\"The fusion-based set of binary models reaches 91.04% global accuracy, which is higher than the classic weighted-vote system reported as 89.62%.\"}]","Detection of Malignant Skin Lesions Based on Decision Fusion of Ensembles of Neural Networks | PDF",1790253396,48]