Deep Transfer Learning with Fused Optimal Features for Detection of Diabetic Foot Ulcers-Reference-Cited by-同舟云学术

Deep Transfer Learning with Fused Optimal Features for Detection of Diabetic Foot Ulcers

Published:2023-11-10 Issue:3 Volume:1 Page:37-48
ISSN:3005-480X
Container-title:International Journal of Clinical Medical Research
language:en
Short-container-title:IJCMR

Author:

Rajinikanth Venkatesan

Abstract

Abstract: Background: As a result of the availability of high-speed computing devices, disease screening procedures in modern hospitals have significantly improved over the last few decades. As a result of this invention of deep learning procedures (DP), this work implemented modern diagnostic schemes to achieve accurate and fast results when screening patients for diseases with the aid of medical data. Method: This study applied pre-trained DP to detect Diabetic Foot Ulcers (DFU) from the test images. This work consists following stages; (i) Resizing, augmenting, and enhancing images, (ii) deep-features mining with a chosen DP, (iii) features reduction using 50% dropout and serial features-fusion, and (iv) Binary-classification through five-fold cross-validation. Two types of disease detection procedures implemented during the investigation: (a) Conventional deep-features and (b) fused deep-features (FD). Result: As a result of this study, the FD obtained with VGG16 and ResNet101 enabled 99.5% accuracy in DFU detection using SoftMax classifier. Conclusion: This demonstration confirmed that the proposed scheme is effective in detecting DFU from the chosen database.

Publisher

Pharaoh Academy International Publishing Co., Limited

Link

https://www.pharaohacademy.com/1/3/14/pdf

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