Vitiligo disease prediction using K-mean, GLCM and voting classification

Saini, Komalpreet; Singh, Surender

doi:10.1063/5.0109172

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…The classification has been done using the ImageNet model, which has shown an accuracy result of 92.91%. In Saini & Singh (2022), the authors have used two classifiers, namely, the K-nearest neighbour (KNN) and the voting classifier, for predicting the existence of vitiligo skin disease. The dataset has been divided into two parts, i.e., train and test.…”

Section: Literature Review On Machine Learning and Deep Learning Mode...mentioning

confidence: 99%

Deep Learning based Model for Detection of Vitiligo Skin Disease using Pre-trained Inception V3

Sharma,

Guleria,

Kumar

et al. 2023

Int. j. math. eng. manag. sci.

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Skin diseases are commonly identified problems all over the world. There are various kinds of skin diseases, such as skin cancer, vulgaris, ichthyosis, and eczema. Vitiligo is one of the skin diseases that can occur in any area of the body, including the inner part of the mouth. This type of skin can have immense negative impacts on the human body, involving memory issues, hypertension, and mental health problems. Conventionally, dermatologists use biopsy, blood tests, and patch testing to identify the presence of skin diseases and provide medications to patients. However, these treatments don't always provide results due to the transformation of a macule into a patch. Various machine learning (ML) and deep learning (DL) models have been developed for the early identification of macules to avoid delays in treatments. This work has implemented a DL-based model for predicting and classifying vitiligo skin disease in healthy skin. The features from the images have been extracted using a pre-trained Inception V3 model and substituted for each classifier, namely, naive Bayes, convolutional neural network (CNN), random forest, and decision tree. The results have been determined as accuracy, recall, precision, area under the curve (AUC), and F1-score for Inception V3 with naive Bayes as 99.5%, 0.995, 0.995, 0.997, and 0.995, respectively. The Inception V3 with CNN has achieved 99.8% accuracy, 0.998 recall, 0.998 precision, 1.00 AUC, and 0.998 F1-score. Further, Inception V3 with random forest shows 99.9% accuracy, 0.999 recall, 0.999 precision, 1.00 AUC, and 0.999 F1-score values whereas, Inception V3 with decision tree classifier shows an accuracy value of 97.8%, 0.978 recall, 0.977 precision, 0.969 AUC, and 0.977 F1-score. Results exhibit that Inception V3 with a random forest classifier outperforms in terms of accuracy, recall, precision, and F1-score, whereas for the AUC metric, Inception V3 with a random forest and Inception V3 with CNN have shown the same outcomes of 1.00.

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Section: Literature Review On Machine Learning and Deep Learning Mode...mentioning

confidence: 99%

Deep Learning based Model for Detection of Vitiligo Skin Disease using Pre-trained Inception V3

Sharma,

Guleria,

Kumar

et al. 2023

Int. j. math. eng. manag. sci.

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Non-invasive skin measurement methods and diagnostics for vitiligo: a systematic review

Abdi¹,

Anthony²,

Farkouh³

et al. 2023

Front. Med.

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Vitiligo is a multifaceted autoimmune depigmenting disorder affecting around 0.5 to 2.0% of individuals globally. Standardizing diagnosis and therapy tracking can be arduous, as numerous clinical evaluation methods are subject to interobserver variability and may not be validated. Therefore, there is a need for diagnostic tools that are objective, dependable, and preferably non-invasive.AimsThis systematic review provides a comprehensive overview of the non-invasive objective skin measurement methods that are currently used to evaluate the diagnosis, severity, and progression of vitiligo, as well as the advantages and limitations of each technique.MethodsThe Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist was used for the systematic review. Scopus, Embase, Cochrane Library, and Web of Science databases were comprehensively searched for non-invasive imaging and biophysical skin measuring methods to diagnose, evaluate the severity of, or monitor the effects of vitiligo treatment. The risk of bias in included articles was assessed using the QUADAS-2 quality assessment scale.ResultsAn extensive literature search resulted in 64 studies for analysis, describing eight imaging techniques (reflectance confocal microscopy, computer-aided imaging analysis, optical coherence tomography, infrared photography, third-harmonic generation microscopy, multiphoton microscopy, ultraviolet light photography, and visible light/digital photograph), and three biophysical approaches (dermoscopy, colorimetry, spectrometry) used in diagnosing and assessing vitiligo. Pertinent information about functionality, mechanisms of action, sensitivity, and specificity was obtained for all studies, and insights into the strengths and limitations of each diagnostic technique were addressed. Methodological study quality was adequate; however, statistical analysis was not achievable because of the variety of methods evaluated and the non-standardized reporting of diagnostic accuracy results.ConclusionsThe results of this systematic review can enhance clinical practice and research by providing a comprehensive overview of the spectrum of non-invasive imaging and biophysical techniques in vitiligo assessment. Studies with larger sample sizes and sound methodology are required to develop verified methods for use in future practice and research.Systematic review registration(PROSPERO) database, (CRD42023395996).

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Vitiligo disease prediction using K-mean, GLCM and voting classification

Cited by 2 publications

References 23 publications

Deep Learning based Model for Detection of Vitiligo Skin Disease using Pre-trained Inception V3

Deep Learning based Model for Detection of Vitiligo Skin Disease using Pre-trained Inception V3

Non-invasive skin measurement methods and diagnostics for vitiligo: a systematic review

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