A Robust Ensemble of Convolutional Neural Networks for the Detection of Monkeypox Disease from Skin Images

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 16; p. 7134
• Main Authors: Muñoz-Saavedra, Luis, Escobar-Linero, Elena, Civit-Masot, Javier, Luna-Perejón, Francisco, Civit, Antón, Domínguez-Morales, Manuel
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.08.2023; MDPI
• Subjects: Accuracy; Algorithms; Artificial intelligence; convolutional neural network; Databases, Factual; Datasets; Deep learning; ensemble; Epidemics; Human monkeypox; Humans; Measles; Medical imaging equipment; Monkeypox; Mpox (monkeypox) - diagnostic imaging; Neural networks; Neural Networks, Computer; Skin; Skin - diagnostic imaging; skin disease; Skin diseases; Smallpox; Viruses; Spain; skin disease; database; ensemble; convolutional neural network; monkeypox
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23167134

Monkeypox is a smallpox-like disease that was declared a global health emergency in July 2022. Because of this resemblance, it is not easy to distinguish a monkeypox rash from other similar diseases; however, due to the novelty of this disease, there are no widely used databases for this purpose with which to develop image-based classification algorithms. Therefore, three significant contributions are proposed in this work: first, the development of a publicly available dataset of monkeypox images; second, the development of a classification system based on convolutional neural networks in order to automatically distinguish monkeypox marks from those produced by other diseases; and, finally, the use of explainable AI tools for ensemble networks. For point 1, free images of monkeypox cases and other diseases have been searched in government databases and processed until we are left with only a section of the skin of the patients in each case. For point 2, various pre-trained models were used as classifiers and, in the second instance, combinations of these were used to form ensembles. And, for point 3, this is the first documented time that an explainable AI technique (like GradCAM) is applied to the results of ensemble networks. Among all the tests, the accuracy reaches 93% in the case of single pre-trained networks, and up to 98% using an ensemble of three networks (ResNet50, EfficientNetB0, and MobileNetV2). Comparing these results with previous work, a substantial improvement in classification accuracy is observed.