Review of medical image recognition technologies to detect melanomas using neural networks

Autor: Alexander Ignatev, Mila Efimenko, Konstantin Koshechkin
Rok vydání: 2020
Předmět:
Skin Neoplasms
Computer science
Convolutional neural network
Review
lcsh:Computer applications to medicine. Medical informatics
Machine learning
computer.software_genre
Biochemistry
Sensitivity and Specificity
World health
03 medical and health sciences
0302 clinical medicine
Deep Learning
Structural Biology
Image Interpretation
Computer-Assisted
medicine
Skin cancer
Humans
Deep learning neural network
lcsh:QH301-705.5
Molecular Biology
Melanoma
Early Detection of Cancer
030304 developmental biology
0303 health sciences
Dermatoscopy
Artificial neural network
medicine.diagnostic_test
business.industry
Applied Mathematics
Melanoma classification
Cancer
medicine.disease
Computer Science Applications
Data Accuracy
Systematic review
lcsh:Biology (General)
030220 oncology & carcinogenesis
lcsh:R858-859.7
Fuzzy clustering algorithm
Artificial intelligence
business
computer
Zdroj: BMC Bioinformatics
BMC Bioinformatics, Vol 21, Iss S11, Pp 1-7 (2020)
ISSN: 1471-2105
Popis: Background Melanoma is one of the most aggressive types of cancer that has become a world-class problem. According to the World Health Organization estimates, 132,000 cases of the disease and 66,000 deaths from malignant melanoma and other forms of skin cancer are reported annually worldwide (https://apps.who.int/gho/data/?theme=main) and those numbers continue to grow. In our opinion, due to the increasing incidence of the disease, it is necessary to find new, easy to use and sensitive methods for the early diagnosis of melanoma in a large number of people around the world. Over the last decade, neural networks show highly sensitive, specific, and accurate results. Objective This study presents a review of PubMed papers including requests «melanoma neural network» and «melanoma neural network dermatoscopy». We review recent researches and discuss their opportunities acceptable in clinical practice. Methods We searched the PubMed database for systematic reviews and original research papers on the requests «melanoma neural network» and «melanoma neural network dermatoscopy» published in English. Only papers that reported results, progress and outcomes are included in this review. Results We found 11 papers that match our requests that observed convolutional and deep-learning neural networks combined with fuzzy clustering or World Cup Optimization algorithms in analyzing dermatoscopic images. All of them require an ABCD (asymmetry, border, color, and differential structures) algorithm and its derivates (in combination with ABCD algorithm or separately). Also, they require a large dataset of dermatoscopic images and optimized estimation parameters to provide high specificity, accuracy and sensitivity. Conclusions According to the analyzed papers, neural networks show higher specificity, accuracy and sensitivity than dermatologists. Neural networks are able to evaluate features that might be unavailable to the naked human eye. Despite that, we need more datasets to confirm those statements. Nowadays machine learning becomes a helpful tool in early diagnosing skin diseases, especially melanoma.
Databáze: OpenAIRE
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