Detection of Vitiligo Skin Disease using LVQ Neural Network

Anthal, Jyotsna; Upadhyay, Anand; Gupta, Ashish

doi:10.1109/ctceec.2017.8455029

Cited by 13 publications

(4 citation statements)

References 3 publications

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“…This milestone underscores the substantial progress made by DL algorithms in the field of dermatological classification, especially in the realm of neoplastic and inflammatory skin diseases, for which these algorithms have reached a reputable level of application [127]. For pigmented skin diseases, including vitiligo [112][113][114][115], extant classification algorithms are capable of achieving diagnostic accuracies exceeding 85%. This accomplishment is undeniably encouraging.…”

Section: Analysis Of Resultsmentioning

confidence: 98%

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Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review

Zhang,

Zhong,

et al. 2023

Diagnostics

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Objective: Skin diseases constitute a widespread health concern, and the application of machine learning and deep learning algorithms has been instrumental in improving diagnostic accuracy and treatment effectiveness. This paper aims to provide a comprehensive review of the existing research on the utilization of machine learning and deep learning in the field of skin disease diagnosis, with a particular focus on recent widely used methods of deep learning. The present challenges and constraints were also analyzed and possible solutions were proposed. Methods: We collected comprehensive works from the literature, sourced from distinguished databases including IEEE, Springer, Web of Science, and PubMed, with a particular emphasis on the most recent 5-year advancements. From the extensive corpus of available research, twenty-nine articles relevant to the segmentation of dermatological images and forty-five articles about the classification of dermatological images were incorporated into this review. These articles were systematically categorized into two classes based on the computational algorithms utilized: traditional machine learning algorithms and deep learning algorithms. An in-depth comparative analysis was carried out, based on the employed methodologies and their corresponding outcomes. Conclusions: Present outcomes of research highlight the enhanced effectiveness of deep learning methods over traditional machine learning techniques in the field of dermatological diagnosis. Nevertheless, there remains significant scope for improvement, especially in improving the accuracy of algorithms. The challenges associated with the availability of diverse datasets, the generalizability of segmentation and classification models, and the interpretability of models also continue to be pressing issues. Moreover, the focus of future research should be appropriately shifted. A significant amount of existing research is primarily focused on melanoma, and consequently there is a need to broaden the field of pigmented dermatology research in the future. These insights not only emphasize the potential of deep learning in dermatological diagnosis but also highlight directions that should be focused on.

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Section: Analysis Of Resultsmentioning

confidence: 98%

“…Further assessments are imperative to determine its applicability to other ethnic skin types. An alternative approach for vitiligo detection involves the utilization of the Learning Vector Quantization (LVQ) neural network [115]. LVQ is an artificial neural algorithm based on supervised learning that is trained using known data.…”

Section: Deep Learningmentioning

confidence: 99%

Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review

Zhang,

Zhong,

et al. 2023

Diagnostics

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“…Two earlier studies reported deep learning models that achieved a sensitivity of 92% and 86%, respectively, for the classification of vitiligo (36,37). However, both studies were trained on small sample sets and are yet unvalidated.…”

Section: Discussionmentioning

confidence: 99%

A deep learning-based hybrid artificial intelligence model for the detection and severity assessment of vitiligo lesions

Guo¹,

Yang²,

Ding³

et al. 2022

Ann Transl Med

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Background: We aimed to establish and validate a deep learning-based hybrid artificial intelligence (AI) model for the objective morphometric and colorimetric assessment of vitiligo lesions. Methods: Two main datasets containing curated images of vitiligo lesions from Chinese patients (Fitzpatrick skin types III or IV) were established, including one with 2,720 images for lesion localization study and the other with 1,262 images for lesion segmentation study. Besides, an additional test set containing 145 images of vitiligo lesions from other Fitzpatrick skin types (I, II, or V) was also generated. A 3-stage hybrid model was constructed. YOLO v3 (You Only Look Once, v3) architecture was trained and validated to classify and localize vitiligo lesions, with sensitivity and error rate as primary performance outcomes. Then a segmentation study comparing 3 deep convolutional neural networks (DCNNs), Pyramid Scene Parsing Network (PSPNet), UNet, and UNet++, was carried out based on the Jaccard index (JI). The architecture with the best performance was integrated into the model. Three add-on metrics, namely VAreaA, VAreaR, and VColor were finally developed to measure absolute, relative size changes and pigmentation, respectively.Agreement between the AI model and dermatologist evaluators were assessed. Results:The sensitivity of the YOLO v3 architecture to detect vitiligo lesions was 92.91% with an error rate of 14.98%. The UNet++ architecture outperformed the others in the segmentation study (JI, 0.79) and was integrated into the model. On the additional test set, however, the model achieved a lower detection sensitivity (72.41%) and a lower segmentation score (JI, 0.69). With respect to size changes, no difference was observed between the AI model, trained dermatologists (W=0.812, P<0.05), and Photoshop analysis (P=0.075, P=0.212 respectively), which all displayed good concordance. Conclusions:We developed a novel, convenient, objective, and quantitative deep learning-based hybrid model which simultaneously evaluated both morphometric and colorimetric vitiligo lesions from patients with Fitzpatrick skin types III or IV, rendering it suitable for the assessment of severity of vitiligo lesions in Asians in both clinic and research scenarios. More work is also warranted for its use in other ethnic skin groups.

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“…Research on computer vision was also conducted to detect areas affected by skin diseases or called Vitiligo with the help of images taken by the camera and classify the affected area [6], the implementation of LVQ neural networks provided good accuracy of 92.22% and kappa values 0.810.…”

Section: Introductionmentioning

confidence: 99%

Detection of Industrial Machine Work Errors using LVQ Neural Network

Rasyid¹,

Tahir²,

Syafaruddin³

2019

Proceedings of the 1st International Conference on Science and Technology, ICOST 2019, 2-3 May, Makassar, Indonesia

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In the world of industry, the utilization of technology machinery industry is one of the most important factors to facilitate the employment of human. However, an industrial machine does not work regardless of fault that can inhibit the production process and cause harm to the industry. This paper aims to detect errors with the industrial machine work to analyze the movement of industrial machinery in a video, at this stage of the process of preprocessing, image resizes, do segmentation method thresholding, and the morphological operations with the opening operation. The further step, the feature extraction performed by converting a binary image into vector data is used as input data in the classification process using Algorithm Learning Vector Quantization (LVQ) Neural Network version 1 and version 2. Research results obtained detection accuracy reached 100% for training using LVQ1 much higher than the results of the training using LVQ2 with an accuracy of only 67.59%.

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Detection of Vitiligo Skin Disease using LVQ Neural Network

Cited by 13 publications

References 3 publications

Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review

Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review

A deep learning-based hybrid artificial intelligence model for the detection and severity assessment of vitiligo lesions

Detection of Industrial Machine Work Errors using LVQ Neural Network

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