Deep-CNN with Bacterial Foraging Optimization Based Cascaded Hybrid Structure for Diabetic Foot Ulcer Screening

Naif Al Mudawi, Wahidur Rahman, Md. Bappy

2024

Abstract

Diabetic Foot Ulcer (DFU) poses a challenge for healing as a result of inadequate blood circulation and susceptibility to infections. Untreated DFU can result in serious complications, such as the necessity for lower limb amputation, which has a substantial impact on one’s quality of life. Although several systems have been created to recognize or categorize DFU using different technologies, only a few have integrated Machine Learning (ML), Deep Learning (DL), and optimization techniques. This study presents a novel method that utilizes sophisticated algorithms to precisely detect Diabetic Foot Ulcers (DFU) from photographs. The study is organized into distinct phases: generating a dataset, extracting features from DFU photos using pre-trained Convolutional Neural Networks (CNN), identifying the most effective features through an optimization technique, and categorizing the images using standard Machine Learning algorithms. The dataset is divided into photos that are DFU-positive and images that are DFU-negative. The Bacterial Foraging Optimization (BFO) approach is used to choose crucial features following their extraction from the CNN. Subsequently, seven machine learning techniques are employed to accurately classify the photos. The effectiveness of this strategy has been evaluated through the collection and analysis of experimental data. The proposed method achieved a remarkable 100% accuracy in classifying DFU images by utilizing a combination of EfficientNetB0, Logistic Regression Classifier, and BFO algorithms. The research also contrasts this novel methodology with prior methodologies, showcasing its potential for practical DFU identification in real-world scenarios.

Paper Citation

in Harvard Style

Al Mudawi N., Rahman W. and Bappy M. (2024). Deep-CNN with Bacterial Foraging Optimization Based Cascaded Hybrid Structure for Diabetic Foot Ulcer Screening. In Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-717-7, SciTePress, pages 285-292. DOI: 10.5220/0012931300003822

in Bibtex Style

@conference{icinco24,
author={Naif Al Mudawi and Wahidur Rahman and Md. Bappy},
title={Deep-CNN with Bacterial Foraging Optimization Based Cascaded Hybrid Structure for Diabetic Foot Ulcer Screening},
booktitle={Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2024},
pages={285-292},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012931300003822},
isbn={978-989-758-717-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - Deep-CNN with Bacterial Foraging Optimization Based Cascaded Hybrid Structure for Diabetic Foot Ulcer Screening
SN - 978-989-758-717-7
AU - Al Mudawi N.
AU - Rahman W.
AU - Bappy M.
PY - 2024
SP - 285
EP - 292
DO - 10.5220/0012931300003822
PB - SciTePress