Diabetic foot ulcer classification using mapped binary patterns and convolutional neural networks

Al-Garaawi, Nora; Ebsim, Raja; Alharan, Abbas F. H.; Yap, Moi Hoon

doi:10.1016/j.compbiomed.2021.105055

Cited by 40 publications

(24 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, deep features are also important as demonstrated in achievements in several medical and non-medical applications (e.g. DFU diagnosis) [6], [7], [8], [9] [13], [14], [15], [16], [17] [21], [22].…”

Section: Introductionmentioning

confidence: 99%

“…Interestingly, in recent years the researchers' focus has been shifted to combine handcrafted features with deep features which have shown impressive performance in several computer vision tasks and applications such as face detection [23], facial expression recognition [23], age estimation [23], and image recognition [24]. In other words, the models that were trained on the fused features from both the handcrafted and deep features achieved significantly higher accuracy than those trained on the handcrafted features only or deep features only as in [22], [23], [24], [25], [26], [27]. Therefore, in this study, we propose to use the fused features from both the handcrafted and deep features for the recognition of DFU with the presence of ischaemia and infection.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fusion of Hand-crafted and Deep Features for Automatic Diabetic Foot Ulcer Classification

Al-Garaawi¹,

Harbi²,

Morris³

2022

TEM Journal

Self Cite

View full text Add to dashboard Cite

This paper proposes to combine both the texture and deep features to build a robust diabetic foot ulcer recognition system since both features represent valuable information about the disease. The proposed system consists of three stages: feature extraction, feature fusion, and DFU classification. The feature extraction is performed by extracting the handcrafted and deep features. The feature fusion is performed by concatenating both feature vectors into a single vector. The DFU classification is performed by training a random forest classifier on the fusion vectors and the resulting classifier is used then for classification. Experimental results showed that the proposed approach provides satisfactory performance in DFU, ischaemia, and infection classification.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fusion of Hand-crafted and Deep Features for Automatic Diabetic Foot Ulcer Classification

Al-Garaawi¹,

Harbi²,

Morris³

2022

TEM Journal

Self Cite

View full text Add to dashboard Cite

show abstract

“…In 2021, the study by Al-Garaawi et al aimed at developing a method for DFU classification through the use of CNN, in which texture information on the DFU is used as the model input in addition to a RGB image of the ROI [ 52 ]. In particular, the ability to discriminate between healthy subjects and subjects affected by DFU, as well as between ischemia and non-ischemia and infection and non-infection, was evaluated.…”

Section: Artificial Intelligence In Diabetic Foot Syndrome: Methodolo...mentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

Diabetic foot syndrome is a multifactorial pathology with at least three main etiological factors, i.e., peripheral neuropathy, peripheral arterial disease, and infection. In addition to complexity, another distinctive trait of diabetic foot syndrome is its insidiousness, due to a frequent lack of early symptoms. In recent years, it has become clear that the prevalence of diabetic foot syndrome is increasing, and it is among the diabetes complications with a stronger impact on patient’s quality of life. Considering the complex nature of this syndrome, artificial intelligence (AI) methodologies appear adequate to address aspects such as timely screening for the identification of the risk for foot ulcers (or, even worse, for amputation), based on appropriate sensor technologies. In this review, we summarize the main findings of the pertinent studies in the field, paying attention to both the AI-based methodological aspects and the main physiological/clinical study outcomes. The analyzed studies show that AI application to data derived by different technologies provides promising results, but in our opinion future studies may benefit from inclusion of quantitative measures based on simple sensors, which are still scarcely exploited.

show abstract

“…A large public dataset, composed of 4000 images with ground truth labeling, was released for the Diabetic Foot Ulcers Grand Challenge (DFUC 2020) aiming to improve the detection accuracy in a real-world scenario and to accelerate the development of innovative approaches [ 6 ]. In addition, extensive literature can be found for DFU localization and detection [ 11 ], as well as wound classification [ 12 , 13 , 14 ]. Furthermore, remote, noncontact, and automated DFU detection may be plausible using mobile and cloud technologies [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Feature Ranking by Variational Dropout for Classification Using Thermograms from Diabetic Foot Ulcers

Hernandez-Guedes

Arteaga-Marrero

Villa

et al. 2023

Sensors

View full text Add to dashboard Cite

Diabetes mellitus presents a high prevalence around the world. A common and long-term derived complication is diabetic foot ulcers (DFUs), which have a global prevalence of roughly 6.3%, and a lifetime incidence of up to 34%. Infrared thermograms, covering the entire plantar aspect of both feet, can be employed to monitor the risk of developing a foot ulcer, because diabetic patients exhibit an abnormal pattern that may indicate a foot disorder. In this study, the publicly available INAOE dataset composed of thermogram images of healthy and diabetic subjects was employed to extract relevant features aiming to establish a set of state-of-the-art features that efficiently classify DFU. This database was extended and balanced by fusing it with private local thermograms from healthy volunteers and generating synthetic data via synthetic minority oversampling technique (SMOTE). State-of-the-art features were extracted using two classical approaches, LASSO and random forest, as well as two variational deep learning (DL)-based ones: concrete and variational dropout. Then, the most relevant features were detected and ranked. Subsequently, the extracted features were employed to classify subjects at risk of developing an ulcer using as reference a support vector machine (SVM) classifier with a fixed hyperparameter configuration to evaluate the robustness of the selected features. The new set of features extracted considerably differed from those currently considered state-of-the-art but provided a fair performance. Among the implemented extraction approaches, the variational DL ones, particularly the concrete dropout, performed the best, reporting an F1 score of 90% using the aforementioned SVM classifier. In comparison with features previously considered as the state-of-the-art, approximately 15% better performance was achieved for classification.

show abstract

Diabetic foot ulcer classification using mapped binary patterns and convolutional neural networks

Cited by 40 publications

References 44 publications

Fusion of Hand-crafted and Deep Features for Automatic Diabetic Foot Ulcer Classification

Fusion of Hand-crafted and Deep Features for Automatic Diabetic Foot Ulcer Classification

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

Feature Ranking by Variational Dropout for Classification Using Thermograms from Diabetic Foot Ulcers

Contact Info

Product

Resources

About