Automated Detection of Infection in Diabetic Foot Ulcer Images Using Convolutional Neural Network

Abstract

A bacterial or bone infection in the feet causes diabetic foot infection (DFI), which results in reddish skin in the wound and surrounding area. DFI is the most prevalent and dangerous type of diabetic mellitus. It will mainly occur in people with heart disease, renal illness, or eye disease. The clinical signs and symptoms of local inflammation are used to diagnose diabetic foot infection. In assessing diabetic foot ulcers, the infection has significant clinical implications in predicting the likelihood of amputation. In this work, a diabetic foot infection network (DFINET) is proposed to assess infection and no infection from diabetic foot ulcer images. A DFINET consists of 22 layers with a unique parallel convolution layer with ReLU, a normalization layer, and a fully connected layer with a dropout connection. Experiments have shown that the DFINET, when combined with this technique and improved image augmentation, should yield promising results in infection recognition, with an accuracy of 91.98%, and a Matthews correlation coefficient of 0.84 on binary classification. Such enhancements to existing methods shows that the suggested approach can assist medical experts in automated detection of DFI.

Towards Home-Based Diabetic Foot Ulcer Monitoring: A Systematic Review

“…Three articles reported [ 32 , 50 , 51 ] the use of zero padding instead of resizing to avoid the distortion of image features. Non-uniform illumination was addressed by applying equalization [ 17 ], normalization [ 51 , 52 , 53 ], standardization [ 22 ], and other illumination adjustment techniques [ 16 ]. Color information improvement was performed by using saturation, gamma correction [ 17 ], and other enhancement techniques [ 54 ].…”

“…Non-uniform illumination was addressed by applying equalization [ 17 ], normalization [ 51 , 52 , 53 ], standardization [ 22 ], and other illumination adjustment techniques [ 16 ]. Color information improvement was performed by using saturation, gamma correction [ 17 ], and other enhancement techniques [ 54 ]. Sharpening [ 17 , 53 ], local binary patterns (LBPs) [ 46 ], and contrast-limited adaptive histogram equalization (CLAHE) [ 16 , 55 ] techniques were used for image contrast adjustment.…”

“…Color information improvement was performed by using saturation, gamma correction [ 17 ], and other enhancement techniques [ 54 ]. Sharpening [ 17 , 53 ], local binary patterns (LBPs) [ 46 ], and contrast-limited adaptive histogram equalization (CLAHE) [ 16 , 55 ] techniques were used for image contrast adjustment. Noisy images were improved by using median filtering [ 56 ] or other noise removal techniques [ 16 ].…”

“…One of the public datasets [ 40 ] contained augmented data that were preprocessed before augmentation. As a result, the articles that used this dataset did not used any preprocessing or augmentation techniques [ 17 , 42 , 43 , 44 , 45 ].…”

Artificial Intelligence Methodologies Applied to Technologies for Screening, Diagnosis and Care of the Diabetic Foot: A Narrative Review

“…In the study by Yogapriya et al, clinical signs and symptoms of local inflammation were used to diagnose diabetic foot infection, in the hypothesis that infections have significant implications in predicting the likelihood of amputation in DFU [ 59 ]. The analyzed dataset consisted of 5890 DFU images, with 2945 images for infection and 2945 images for non-infection.…”