On using Support Vector Machines for the Detection and Quantification of Hand Eczema

Schnürle, Stefan; Pouly, Marc; Brück, Tim vor der; Navarini, Alexander; Koller, Thomas

doi:10.5220/0006125000750084

Cited by 8 publications

(3 citation statements)

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“…Other work related to hand segmentation focused either on hand detection, 21 palm region extraction for biometrics, 22 gesture recognition 23 or bone segmentation from ultrasound and MRI scans 24,25 . Previous work on automated eczema severity assessment were based on smaller data sets and mainly proposed lesion segmentation approaches, 26 some with classification of the overall severity level 27–29 . One study's approach consisted in the detection (as opposed to segmentation) of atopic eczema lesions based on 1393 patients' pictures followed by the severity classification of seven clinical signs 30 .…”

Section: Discussionmentioning

confidence: 99%

Objective hand eczema severity assessment with automated lesion anatomical stratification

Amruthalingam

Mang

Gottfrois

et al. 2023

Experimental Dermatology

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Hand eczema (HE), also called hand dermatitis, is an inflammatory disease, often chronic, causing a wide spectrum of symptoms including redness (erythema), scaling, hyperkeratosis, fissures, vesicles and erosions. 1 All these features are visible on digital pictures. It is one of the most frequent dermatoses with 15% life prevalence and 10% 1-year prevalence in the general population. It has a multifactorial aetiology including both environmental and genetic factors. 2 HE severity range spans from mild to severe cases, the latter causing adverse physical and psychological effects, both in private and professional activities, and significant impairment to patients' quality

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Section: Discussionmentioning

confidence: 99%

Objective hand eczema severity assessment with automated lesion anatomical stratification

Amruthalingam

Mang

Gottfrois

et al. 2023

Experimental Dermatology

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“…Some studies [ 21 – 23 ] chose to rely on classification DLMs, thus capping the achievable precision to discrete scores in contrast to our DLM, which predicts continuous metrics. Various segmentation approaches have also been applied to ulcers [ 24 ], skin cancer [ 25 , 26 ], eczema [ 27 ], and psoriasis [ 7 , 28 ], and therefore could also be used to produce metrics similar to our study. However, they all targeted diseases with plaques, single lesions, or lesions larger than PP efflorescences.…”

Section: Discussionmentioning

confidence: 99%

Quantification of Efflorescences in Pustular Psoriasis Using Deep Learning

Amruthalingam¹,

Buerzle

Gottfrois

et al. 2022

Healthc Inform Res

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Objectives: Pustular psoriasis (PP) is one of the most severe and chronic skin conditions. Its treatment is difficult, and measurements of its severity are highly dependent on clinicians’ experience. Pustules and brown spots are the main efflorescences of the disease and directly correlate with its activity. We propose an automated deep learning model (DLM) to quantify lesions in terms of count and surface percentage from patient photographs. Methods: In this retrospective study, two dermatologists and a student labeled 151 photographs of PP patients for pustules and brown spots. The DLM was trained and validated with 121 photographs, keeping 30 photographs as a test set to assess the DLM performance on unseen data. We also evaluated our DLM on 213 unstandardized, out-of-distribution photographs of various pustular disorders (referred to as the pustular set), which were ranked from 0 (no disease) to 4 (very severe) by one dermatologist for disease severity. The agreement between the DLM predictions and experts’ labels was evaluated with the intraclass correlation coefficient (ICC) for the test set and Spearman correlation (SC) coefficient for the pustular set. Results: On the test set, the DLM achieved an ICC of 0.97 (95% confidence interval [CI], 0.97–0.98) for count and 0.93 (95% CI, 0.92–0.94) for surface percentage. On the pustular set, the DLM reached a SC coefficient of 0.66 (95% CI, 0.60–0.74) for count and 0.80 (95% CI, 0.75–0.83) for surface percentage. Conclusions: The proposed method quantifies efflorescences from PP photographs reliably and automatically, enabling a precise and objective evaluation of disease activity.

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“…Artificial Neural Network(ANN) and Support Vector Machine (SVM) classifiers were compared and the best performance they achieved was 78% specificity on a dataset consists of 900 images with 173 malignant lesions using ANN. Schnurle et al [37] provide an automated approach to classify hand eczema. For balancing data, they used the oversampling technique and then extract colour, texture and histogram features from the provided images.…”

Section: Literature Reviewmentioning

confidence: 99%

An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions

Hameed

Shabut

et al. 2019

Computers

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Skin diseases cases are increasing on a daily basis and are difficult to handle due to the global imbalance between skin disease patients and dermatologists. Skin diseases are among the top 5 leading cause of the worldwide disease burden. To reduce this burden, computer-aided diagnosis systems (CAD) are highly demanded. Single disease classification is the major shortcoming in the existing work. Due to the similar characteristics of skin diseases, classification of multiple skin lesions is very challenging. This research work is an extension of our existing work where a novel classification scheme is proposed for multi-class classification. The proposed classification framework can classify an input skin image into one of the six non-overlapping classes i.e., healthy, acne, eczema, psoriasis, benign and malignant melanoma. The proposed classification framework constitutes four steps, i.e., pre-processing, segmentation, feature extraction and classification. Different image processing and machine learning techniques are used to accomplish each step. 10-fold cross-validation is utilized, and experiments are performed on 1800 images. An accuracy of 94.74% was achieved using Quadratic Support Vector Machine. The proposed classification scheme can help patients in the early classification of skin lesions.

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On using Support Vector Machines for the Detection and Quantification of Hand Eczema

Cited by 8 publications

References 0 publications

Objective hand eczema severity assessment with automated lesion anatomical stratification

Objective hand eczema severity assessment with automated lesion anatomical stratification

Quantification of Efflorescences in Pustular Psoriasis Using Deep Learning

An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions

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