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Deep Learning Perspectives for Prediction of Diabetic Foot Ulcers

Deep Learning Perspectives for Prediction of Diabetic Foot Ulcers

Aman Sharma, Archit Kaushal, Kartik Dogra, Rajni Mohana
Copyright: © 2024 |Pages: 26
ISBN13: 9781668498231|ISBN10: 1668498235|EISBN13: 9781668498248
DOI: 10.4018/978-1-6684-9823-1.ch006
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MLA

Sharma, Aman, et al. "Deep Learning Perspectives for Prediction of Diabetic Foot Ulcers." Metaverse Applications for Intelligent Healthcare, edited by Loveleen Gaur and Noor Zaman Jhanjhi, IGI Global, 2024, pp. 203-228. https://doi.org/10.4018/978-1-6684-9823-1.ch006

APA

Sharma, A., Kaushal, A., Dogra, K., & Mohana, R. (2024). Deep Learning Perspectives for Prediction of Diabetic Foot Ulcers. In L. Gaur & N. Jhanjhi (Eds.), Metaverse Applications for Intelligent Healthcare (pp. 203-228). IGI Global. https://doi.org/10.4018/978-1-6684-9823-1.ch006

Chicago

Sharma, Aman, et al. "Deep Learning Perspectives for Prediction of Diabetic Foot Ulcers." In Metaverse Applications for Intelligent Healthcare, edited by Loveleen Gaur and Noor Zaman Jhanjhi, 203-228. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/978-1-6684-9823-1.ch006

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Abstract

A significant complication of diabetes mellitus, diabetic foot ulcers (DFUs), can have devastating repercussions if they are not identified and treated right away. Machine learning algorithms have gained more attention recently for their potential to anticipate DFUs before they manifest, enabling early management and preventing consequences. In this chapter, the authors examine how convolutional neural networks (CNNs) can be used to forecast DFUs. The performance of DenseNet, EfficientNet, and a regular CNN are specifically compared. With labels identifying the presence or absence of a DFU, the authors use a dataset of medical photographs of diabetic feet to train each model. The objective is to assess the effectiveness of these models and look at how each layer affects the precision of the predictions. The authors also hope to provide some light on how the algorithms are able to pinpoint foot regions that are most likely to get DFUs. They also look into how each CNN model's different layers affect prediction accuracy.

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