ReviewFrontiers in medicine2019
The Possibility of Deep Learning-Based, Computer-Aided Skin Tumor Classifiers.
Review in Frontiers in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 84 citations in OpenAlex.
- Impact of fine-tuning parameters of convolutional neural network for skin cancer detection.Scientific reports · 2025Article
- Artificial Intelligence Smartphone Application for Detection of Simulated Skin Changes: An In Vivo Pilot Study.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2024Article
- Enhancing Skin Cancer Detection and Classification in Dermoscopic Images through Concatenated MobileNetV2 and Xception Models.Bioengineering (Basel, Switzerland) · 2023Article
- Agreement Between Experts and an Untrained Crowd for Identifying Dermoscopic Features Using a Gamified App: Reader Feasibility Study.JMIR medical informatics · 2023Article
- MSCDNet-based multi-class classification of skin cancer using dermoscopy images.PeerJ. Computer science · 2023Article
- MAC-ResNet: Knowledge Distillation Based Lightweight Multiscale-Attention-Crop-ResNet for Eyelid Tumors Detection and Classification.Journal of personalized medicine · 2022Article
- InSiNet: a deep convolutional approach to skin cancer detection and segmentation.Medical & biological engineering & computing · 2022Article
- SkinNet-16: A deep learning approach to identify benign and malignant skin lesions.Frontiers in oncology · 2022Article
- A shallow deep learning approach to classify skin cancer using down-scaling method to minimize time and space complexity.PloS one · 2022Article
- In-Person Verification of Deep Learning Algorithm for Diabetic Retinopathy Screening Using Different Techniques Across Fundus Image Devices.Translational vision science & technology · 2021Article
- Design and Assessment of Convolutional Neural Network Based Methods for Vitiligo Diagnosis.Frontiers in medicine · 2021Article
- Skin Diseases Classification Using Deep Leaning Methods.Current health sciences journalArticle
- Next-Generation Technologies in Dermatology: Use of Artificial Intelligence and Mobile Applications.Indian journal of dermatologyArticle
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The incidence of skin tumors has steadily increased. Although most are benign and do not affect survival, some of the more malignant skin tumors present a lethal threat if a delay in diagnosis permits them to become advanced. Ideally, an inspection by an expert dermatologist would accurately detect malignant skin tumors in the early stage; however, it is not practical for every single patient to receive intensive screening by dermatologists. To overcome this issue, many studies are ongoing to develop dermatologist-level, computer-aided diagnostics. Whereas, many systems that can classify dermoscopic images at this dermatologist-equivalent level have been reported, a much fewer number of systems that can classify conventional clinical images have been reported thus far. Recently, the introduction of deep-learning technology, a method that automatically extracts a set of representative features for further classification has dramatically improved classification efficacy. This new technology has the potential to improve the computer classification accuracy of conventional clinical images to the level of skilled dermatologists. In this review, this new technology and present development of computer-aided skin tumor classifiers will be summarized.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.