Skin Lesion Primary Morphology Classification With End-To-End Deep Learning Network

Polevaya, Tatyana; Ravodin, R A; Filchenkov, Andrey

doi:10.1109/icaiic.2019.8668980

Cited by 11 publications

(3 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They demonstrated that the discordance between the CNN and expert pathologist was comparable with that between different pathologists as reported in the literature. Polevaya et al [196] utilized the pretrained VGG-16 network to classify primary morphology images of macule, nodule, papule and plaque. Experimental results showed that the method was able to achieve an accuracy of 77.50% for 4 classes and 81.67% for 3 classes on the testing dataset.…”

Section: Skin Disease Classificationmentioning

confidence: 99%

“…Transfer learning [101,234] and domain adaptation [235,236,237] have been exploited to deal with the issues caused by lack of large-scale labeled data. As presented above, there have been many works utilizing transfer learning or domain adaptation techniques to improve the performance of deep learning models in skin disease diagnosis tasks [202,196,193,172]. One way to implement transfer learning is to utilize existing pretrained deep learning models to extract semantic features and perform further learning based on these features [238,239,240].…”

Section: Exploit Transfer Learning and Domain Adaptation For Skin Dis...mentioning

confidence: 99%

See 1 more Smart Citation

Skin disease diagnosis with deep learning: a review

Li¹,

Pan²,

Zhao³

et al. 2020

Preprint

View full text Add to dashboard Cite

Skin cancer is one of the most threatening diseases worldwide. However, diagnosing skin cancer correctly is challenging. Recently, deep learning algorithms have emerged to achieve excellent performance on various tasks. Particularly, they have been applied to the skin disease diagnosis tasks. In this paper, we present a review on deep learning methods and their applications in skin disease diagnosis. We first present a brief introduction to skin diseases and image acquisition methods in dermatology, and list several publicly available skin datasets for training and testing algorithms. Then, we introduce the conception of deep learning and review popular deep learning architectures. Thereafter, popular deep learning frameworks facilitating the implementation of deep learning algorithms and performance evaluation metrics are presented. As an important part of this article, we then review the literature involving deep learning methods for skin disease diagnosis from several aspects according to the specific tasks. Additionally, we discuss the challenges faced in the area and suggest possible future research directions. The major purpose of this article is to provide a conceptual and systematically review of the recent works on skin disease diagnosis with deep learning. Given the popularity of deep learning, there remains great challenges in the area, as well as opportunities that we can explore in the future.

show abstract

Section: Skin Disease Classificationmentioning

confidence: 99%

Section: Exploit Transfer Learning and Domain Adaptation For Skin Dis...mentioning

confidence: 99%

Skin disease diagnosis with deep learning: a review

Li¹,

Pan²,

Zhao³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Then, automatic methods were build to Lung diseases on CT images [10,11]. Moreover, similar methods were also created to analyze skin diseases from skin image [12][13][14][15][16][17][18][19][20][21]. If we take a look in more detail on the method, deep learning has been chosen recently and massively as one of the methods for automatically analyzing the medical image [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Medical vision: web and mobile medical image retrieval system based on google cloud vision

Putra

Asra

Hardijaya

et al. 2020

IJECE

View full text Add to dashboard Cite

The application of information technology is rapidly utilized in the medical system. There is also a massive development in the automatic method for recognizing and detecting objects in the real world. In this study, we present a system called Medical Vision which is designed for people who has no expertise in medical. Medical Vision is a web and mobile-based application to give an initial knowledge in a medical image. This system has 5 features; object detection, web detection, object labeling, safe search, and image properties. These features are run by embedding Google Vision API in the system. We evaluate this system by observing the result of some medical images which inputted into the system. The results showed that our system presents a promising performance and able to give relevant information related to the given image.

show abstract

A Novel Method of Enhancing Skin Lesion Diagnosis Using Attention Mechanisms and Weakly-Supervised Learning

Laddha

2024

Algorithms for Intelligent Systems

View full text Add to dashboard Cite

Skin Lesion Primary Morphology Classification With End-To-End Deep Learning Network

Cited by 11 publications

References 4 publications

Skin disease diagnosis with deep learning: a review

Skin disease diagnosis with deep learning: a review

Medical vision: web and mobile medical image retrieval system based on google cloud vision

A Novel Method of Enhancing Skin Lesion Diagnosis Using Attention Mechanisms and Weakly-Supervised Learning

Contact Info

Product

Resources

About