Diabetic foot ulcer wound tissue detection and classification

Patel, Samsunnisha; Patel, Rachna; Desai, Dhara

doi:10.1109/iciiecs.2017.8276007

Cited by 23 publications

(14 citation statements)

References 5 publications

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“…Then, SVM is implemented, which has obtained precision, ROC, and accuracy of more than 95.2%, 0.946, and 94.6%, respectively, in all the three cases of the considered dataset. Patel et al [26] focused on medical image processing to detect and classify the DFU wound. The four steps involved in the foot ulcer detection system are image preprocessing, image segmentation, feature extraction, texture detection and image classification.…”

Section: Literature Surveymentioning

confidence: 99%

Exploiting Machine Learning Algorithms to Diagnose Foot Ulcers in Diabetic Patients

Reddy¹,

Mahesh²,

Nanjundan³

2021

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Diabetic foot ulcer (DFU) is a complication of diabetes that affects most of the diabetic patients.It will cause open wounds on the foot. Untreated DFU will lead to amputation and infection, which results in removal of foot or leg. As diabetes is the major health problem faced by people of all age groups, identifying foot ulcers at an early stage is essential. In this context, an efficient model to predict the foot ulcer accurately was proposed in this work. OBJECTIVES: To predict DFU using an effective neural network algorithm on a suitable dataset that consists of risk factors and clinical outcomes of the disease. METHODS: In recent days, ML techniques are most commonly used for predicting various diseases. To achieve the objectives a neural network technique, namely extreme learning machine (ELM) is proposed to predict DFU accurately. In addition, three existing algorithms, namely KNN, SVM with Gaussian kernel and ANN are also considered. These are implemented in R programming. RESULTS: Algorithms compared in terms of five evaluation metrics accuracy, zero-one loss, threat score/critical success index (TS/CSI), false omission rate (FOR) and false discovery rate (FDR). The values of accuracy, 0-1 loss, TS/CSI, FOR and FDR obtained for ELM are 96.15%, 0.0385, 0.95, 0 and 0.05 respectively. CONCLUSION: After comparison, it was discovered that ELM had outperformed other algorithms in terms of all the metrics. Thus, it was recommended to use ELM over other algorithms while predicting diabetic foot ulcers.

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Section: Literature Surveymentioning

confidence: 99%

Exploiting Machine Learning Algorithms to Diagnose Foot Ulcers in Diabetic Patients

Reddy¹,

Mahesh²,

Nanjundan³

2021

EAI Endorsed Trans Perv Health Tech

View full text Add to dashboard Cite

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“…Cascaded two steps SVM [15] and YOLOv3 are used to localize DFU [16]. The superpixel method [17], texture, color features [18], and Cloud-based algorithms [19] are used to segment and classify the DFU. The handcrafted features are utilized for DFU classification which is selected manually, whereas in deep learning methodologies features extraction process is performed automatically [20].…”

Section: Related Workmentioning

confidence: 99%

An Integrated Design for Classification and Localization of Diabetic Foot Ulcer Based on CNN and YOLOv2-DFU Models

et al. 2020

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“…Therefore, analysis of diabetes and foot ulcer helps the patients to over come the diabetes disease at early stage. [3] Measuring of acetone level in the human breath is the effective method of early diagnosis of diabetes and also used as a daily monitoring process for diabetes patients. Analysing along with other traditional methods like human blood analysing system the acetone analysis over human breath provides more merits like non invasive, inexpensive and more accurate.…”

Section: Related Workmentioning

confidence: 99%

Non Invasive Method of Diabetes Measurement using Teg Sensor via Foot Skin Temperature

Ganesan

2019

IJEAT

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Abstract: Diabetes is a type of metabolic dieses identified by unstable blood glucose level due to the defect in body to generate or use of insulin. Diabetes is created due to the defect in metabolism of converting the glucose to energy in blood. Hyperglycaemia is a stage were glucose value in the body is greater than 140 mg/dl which leads to type 1 diabetes for the patient. Type 1 diabetes is caused due to lack of generation of insulin in human blood and type 2 diabetes is caused due to resistance to insulin action which leads to several other diseases like foot ulcer and sever wounds in human foot or other parts of the body. Early diagnosing of diabetes disease plays an important task in improving the standard of healthy living. Traditional methods of identifying diabetes does not provides effective results and the results are not more reliable. Temperature based diabetes diagnosing model is defined using TEG sensor to analyse the heat changes in human foot.Imbalanced glucose level affects the performance of nerves system which leads to slower response for temperature change in the foot surface. TEG sensor is used to measure the heat transfer in foot by applying cold water over foot. The rate of temperature changes in foot represents the level of diabetes caused in the patient body. The signals from TEG sensor was collected and processed using signal analysis algorithm using MATLAB software.

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Diabetic foot ulcer wound tissue detection and classification

Cited by 23 publications

References 5 publications

Exploiting Machine Learning Algorithms to Diagnose Foot Ulcers in Diabetic Patients

Exploiting Machine Learning Algorithms to Diagnose Foot Ulcers in Diabetic Patients

An Integrated Design for Classification and Localization of Diabetic Foot Ulcer Based on CNN and YOLOv2-DFU Models

Non Invasive Method of Diabetes Measurement using Teg Sensor via Foot Skin Temperature

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