Authors:
Naif Al Mudawi
1
;
Wahidur Rahman
2
and
Md. Bappy
2
Affiliations:
1
Department of Computer Science, College of Computer Science and Information System, Najran University, Najran, Saudi Arabia
;
2
Department of Computer Science and Engineering, Uttara University, Dhaka, Bangladesh
Keyword(s):
The Diabetic Foot Ulcer, Cascaded Network, Machine Learning, Deep Learning, Bacterial Foraging Optimization.
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.
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