NDLI: Diagnosis and prognosis of melanoma from dermoscopy images using machine learning and deep learning: a systematic literature review

Content Provider	Springer Nature : BioMed Central
Author	Naseri, Hoda Safaei, Ali A.
Abstract	Background Melanoma is a highly aggressive skin cancer, where early and accurate diagnosis is crucial to improve patient outcomes. Dermoscopy, a non-invasive imaging technique, aids in melanoma detection but can be limited by subjective interpretation. Recently, machine learning and deep learning techniques have shown promise in enhancing diagnostic precision by automating the analysis of dermoscopy images. Methods This systematic review examines recent advancements in machine learning (ML) and deep learning (DL) applications for melanoma diagnosis and prognosis using dermoscopy images. We conducted a thorough search across multiple databases, ultimately reviewing 34 studies published between 2016 and 2024. The review covers a range of model architectures, including DenseNet and ResNet, and discusses datasets, methodologies, and evaluation metrics used to validate model performance. Results Our results highlight that certain deep learning architectures, such as DenseNet and DCNN demonstrated outstanding performance, achieving over 95% accuracy on the HAM10000, ISIC and other datasets for melanoma detection from dermoscopy images. The review provides insights into the strengths, limitations, and future research directions of machine learning and deep learning methods in melanoma diagnosis and prognosis. It emphasizes the challenges related to data diversity, model interpretability, and computational resource requirements. Conclusion This review underscores the potential of machine learning and deep learning methods to transform melanoma diagnosis through improved diagnostic accuracy and efficiency. Future research should focus on creating accessible, large datasets and enhancing model interpretability to increase clinical applicability. By addressing these areas, machine learning and deep learning models could play a central role in advancing melanoma diagnosis and patient care.
Related Links	https://bmccancer.biomedcentral.com/counter/pdf/10.1186/s12885-024-13423-y.pdf
Ending Page	25
Page Count	25
Starting Page	1
File Format	HTM / HTML
ISSN	14712407
DOI	10.1186/s12885-024-13423-y
Journal	BMC Cancer
Issue Number	1
Volume Number	25
Language	English
Publisher	BioMed Central
Publisher Date	2025-01-13
Access Restriction	Open
Subject Keyword	Cancer Research Oncology Surgical Oncology Health Promotion and Disease Prevention Biomedicine Medicine Public Health Melanoma Dermoscopy Machine learning Deep learning Convolutional neural networks (CNN) Diagnosis Prognosis Medicine/Public Health
Content Type	Text
Resource Type	Article
Subject	Cancer Research Oncology Genetics
Journal Impact Factor	3.4/2023
5-Year Journal Impact Factor	3.8/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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