Corroboration of skin Diseases: Melanoma, Vitiligo &amp; Vascular Tumor using Transfer Learning

Agrawal, Neha; Aurelia, Sagaya

doi:10.1109/icees51510.2021.9383682

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…To improve efficiency while maintaining low computational cost, the author proposed a data refinement method called knowledge distillation ensemble training (KDE-CT), in which a student network learns from a stronger teacher network. Neha Agrawal et al [5] employed transfer learning techniques to identify three skin diseases, namely melanoma, vitiligo, and vascular lesions. The initial V3 model served as a base model, which was then fine-tuned.…”

Section: IImentioning

confidence: 99%

Hybrid Approach for Skin Disease Classification: Integrating Machine learning and Deep Learning

Shinde¹,

Ingle²

2023

IJRASET

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Skin infections are more common than many other diseases, and they can be caused by various factors such as infectious pollution, microorganisms, awareness, and infections. With the advancement of lasers and photonics-based medical technology, the diagnosis of skin infections has become faster and more accurate. However, the cost of such diagnosis is still limited and expensive. Therefore, image processing techniques are used to develop an automated assessment system for dermatology at an early stage. The extraction of features plays a crucial role in accurately and quickly diagnosing skin diseases. PC vision plays an important role in the detection of skin diseases in various ways. This study focuses on four skin diseases: ringworm, nail parasite, psoriasis, and atopic dermatitis. Convolutional neural networks have achieved close to or even better performance than humans in the imaging field. The skin diseases are classified using a machine learning algorithm, i.e., random forest, which achieves an accuracy of 98.23% after 100 epochs.

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Section: IImentioning

confidence: 99%

Hybrid Approach for Skin Disease Classification: Integrating Machine learning and Deep Learning

Shinde¹,

Ingle²

2023

IJRASET

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“…Murugan et al 48 used classification techniques like support vector machine, probabilistic neural networks, random forest, and combined SVM+ RF classifiers to provide better results in 2020. In 2021, Agrawal and Aurelia 49 proposed an approach to identify three skin diseases, such as melanoma, vitiligo, and vascular tumors, with the inception of V3. Abhishek et al 50 proposed an image‐based prediction of clinical management decisions directly without explicitly predicting the diagnosis.…”

Section: Related Workmentioning

confidence: 99%

HS‐UNET‐ID: An approach for human skin classification integrating between UNET and improved dense convolutional network

Nguyen

Luong

Phan

et al. 2022

Int J Imaging Syst Tech

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The Sun's ultraviolet radiation, toxic chemicals from industry, and other factors can cause problems to human skin. These factors have boosted the risk of humans contracting skin cancer. In identifying and detecting signals of skin cancer diseases, recent advancements in deep learning techniques significantly improve the accuracy of diagnosis. This work delves into processing techniques for human skin images with MorphologyEx (blackhat), thresholding, semantic segmentation with UNET, and several famous convolutional neural network architectures to improve skin classification performance. If skin cancer can be stopped in the first stage, the survival rate of the patient could be more than 95%, while it drops to just 5% if the detection is carried lately in Stage IV4. Due to its seriousness, the healthcare and research community has received considerable attention, and its most important goal is to be able to diagnose it at an early stage. However, the challenge due to the similarity in melanocytic and non-melanoma skin lesions is inevitable. We achieved skin classification performances of 0.8560, 0.9672, 0.8063, and 0.8560 measured by overall accuracy, area under curve, Matthews correlation coefficient, and F1-score, respectively. The proposed approach has been deployed into applications on Mobile and Web platforms.

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“…The system will generate a report indicating if the condition is positive or negative based on the photograph and symptoms provided by the user and will provide short home cures as well as advice the user to visit a dermatologist. • A system based on transfer learning was proposed by Neha et al [15] to distinguish between three forms of dermatological skin diseases: melanoma, vitiligo, and vascular tumor. As a starting point, the Deep learning model Inception V3 was employed.…”

Section: Literature Surveymentioning

confidence: 99%

Development of an Intelligent Vitiligo Detection Classifier

Fayaz¹,

Malhotra²,

Dogra³

2022

Artificial Intelligence and Communication Technologies

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Vitiligo, a widespread depigmenting skin condition is characterized by loss of melanocytes in a selective manner, resulting in nonscaly, chalky-white macules. Vitiligo is a common pigment-degrading skin disease, apparently marked by white patches on the skin and broadly classified into two categories: Segmental vitiligo and Non-segmental vitiligo. An extensive survey of AI-based diagnostic systems for segmental and nonsegmental vitiligo has also been presented. The dermatologist's experience and subjectivity in visual perception of depigmented skin lesions play a big role in Vitiligo diagnosis and its classification. So, there is a dearth need to implement machine learning approaches to improve diagnosis accuracy. Motivated by the fact, the intelligent convolutional neural networks (CNNs) based Vitiligo classification model IVC has been proposed in the research work to classify the Non-segmental vitiligo into its subtypes like Acrofacial, Focal, Generalized and Mucosal vitiligo. The dataset consisting of 507 vitiligo infected images has been collected and manually labelled to accomplish the whole research work.

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Corroboration of skin Diseases: Melanoma, Vitiligo & Vascular Tumor using Transfer Learning

Cited by 8 publications

References 9 publications

Hybrid Approach for Skin Disease Classification: Integrating Machine learning and Deep Learning

Hybrid Approach for Skin Disease Classification: Integrating Machine learning and Deep Learning

HS‐UNET‐ID: An approach for human skin classification integrating between UNET and improved dense convolutional network

Development of an Intelligent Vitiligo Detection Classifier

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