Automated Detection of Infection in Diabetic Foot Ulcer Using Pre-trained Fast Convolutional Neural Network with U++net

A frequent consequence of diabetes and a significant contributor to morbidity and mortality is diabetic foot ulcer (DFU).Early detection and appropriate management of DFUs are essential to prevent complications such as infections and lower extremity amputations. In recent years, medical imaging and...

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Bibliographic Details
Published in:SN computer science Vol. 5; no. 6; p. 705
Main Authors: Murthy, S. V. N., Bhargavi, Kovvuri N., Isaac, Samson, N.Ganesh, E.
Format: Journal Article
Language:English
Published: Singapore Springer Nature Singapore 01.08.2024
Springer Nature B.V
Subjects:
Accuracy
Advanced Computing and Data Sciences
Algorithms
Amputation
Artificial intelligence
Artificial neural networks
Automation
Classification
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Data analysis
Data Structures and Information Theory
Datasets
Diabetes
Foot diseases
Heat detection
Illumination
Image acquisition
Image enhancement
Image quality
Impact analysis
Infections
Information Systems and Communication Service
Ischemia
Leg ulcers
Literature reviews
Machine learning
Medical imaging
Medical research
Neural networks
Original Research
Patients
Pattern Recognition and Graphics
Performance evaluation
Professionals
Quality of life
Shades
Software Engineering/Programming and Operating Systems
Temperature
Ulcers
Vision
Pre-trained fast convolutional neural network
Diabetic foot ulcer
Diabetic foot infection network with the adam optimizer
ISSN:2661-8907, 2662-995X, 2661-8907
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Summary:A frequent consequence of diabetes and a significant contributor to morbidity and mortality is diabetic foot ulcer (DFU).Early detection and appropriate management of DFUs are essential to prevent complications such as infections and lower extremity amputations. In recent years, medical imaging and machine learning have emerged as promising tools for the automated detection and analysis of DFUs. We gathered a sizable foot imaging dataset, including DFU from multiple patients. This paper proposes a novel preprocessing technique based on the Shades of Gray color constancy algorithm to cope with noise and lighting variations in diabetic foot ulcer (DFU) images captured from different devices. The algorithm aims to enhance image quality, improve illumination normalization, and mitigate the impact of noise, thus providing more reliable and accurate DFU analysis and detection. Using the Diabetic Foot Infection Network with the Adam Optimizer (DFINET-AO), features were retrieved after the dataset had been preprocessed and divided. In order to comprehend the normal and pathological spectrum of diabetes, image data and numerical/text data are separated independently. Foot images of patients with aberrant diabetes coverage are separated from each other and classified using Pre-trained Fast Convolutional Neural Network (PFCNN), which has been trained on the U++network. Classification techniques, like foot ulcer analysis, forecast a etiology. This study's primary goal was to establish a novel method for evaluating the likelihood that diabetes individuals may acquire foot ulcers by imaging analysis of existing foot ulcers. The data was preprocessed and segmented after the researchers gathered a collection of foot photographs and medical information from historical records of diabetes patients. The amount of normal and pathological diabetes was then determined from numerical and textual data by extracting characteristics from the segmented data using DFINET-AO. To detect foot ulcers and forecast the chance of diabetic foot ulcer (DFU) formation, foot pictures of patients with aberrant diabetes coverage were pre-trained for rapid convolution using a U++network and classification using a neural network. In this work, we assessed the accuracy of the technique at 99.45% by simulating the diabetic foot ulcer classification and feature extraction outcomes.
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ISSN:2661-8907
2662-995X
2661-8907
DOI:10.1007/s42979-024-02981-4