Frontier machine learning techniques for melanoma skin cancer identification and categorization: An in-Depth review
Skin cancer stands as one of the prevalent and life-threatening malignancies, witnessing a substantial global surge in reported cases. Failure to diagnose it at its incipient stages may lead to metastasis, significantly elevating mortality rates. Early detection, however, presents a curative prospec...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Oral Oncology Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772906024000633 |
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| author | Viomesh Singh Kavita A. Sultanpure Harshwardhan Patil |
| author_facet | Viomesh Singh Kavita A. Sultanpure Harshwardhan Patil |
| author_sort | Viomesh Singh |
| collection | DOAJ |
| description | Skin cancer stands as one of the prevalent and life-threatening malignancies, witnessing a substantial global surge in reported cases. Failure to diagnose it at its incipient stages may lead to metastasis, significantly elevating mortality rates. Early detection, however, presents a curative prospect. Thus, the prompt and precise diagnosis of skin cancers remains a paramount focus in current research. Numerous machine learning techniques have been seamlessly woven into the fascinating world of computer-aided skin cancer diagnosis and figuring out if that blemish is a real troublemaker. Machine learning is like the Sherlock Holmes of artificial intelligence. It's got these brainy models and algorithms that not only soak up information but also play psychic by predicting stuff on brand new data it's never laid eyes on before. In contrast to the conventional biopsy method, which is both laborious and costly, machine learning algorithms offer a viable alternative for early detection, reducing the burden on specialists while concurrently augmenting the diagnostic accuracy of skin lesions. In this article we delve into a thorough exploration of cutting-edge machine learning methods that play a crucial role in identifying those sneaky signs of skin cancer. I took a deep dive into the pool of relevant studies, offering you an insider's look into the performance of the friendly neighborhood the k-nearest neighbor approach, the robust SVM, and the sophisticated CNN. In the review we Openly discussed the inherent limitations and quirks of each algorithm, affording them the spotlight they deserve. |
| format | Article |
| id | doaj-art-028b8bea0d0e470eb8135b3e8ffd5bd8 |
| institution | Kabale University |
| issn | 2772-9060 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Oral Oncology Reports |
| spelling | doaj-art-028b8bea0d0e470eb8135b3e8ffd5bd82025-01-09T06:15:23ZengElsevierOral Oncology Reports2772-90602024-03-019100217Frontier machine learning techniques for melanoma skin cancer identification and categorization: An in-Depth reviewViomesh Singh0Kavita A. Sultanpure1Harshwardhan Patil2Department of Artificial Intelligence and Data Science, Vishwakarma Institute of technology, Pune, IndiaDepartment of information Technology, Vishwakarma Institute of technology, Pune, IndiaDepatment of Information Technolgy, Vishwakarma Institute of Technology, Pune, India; Corresponding author. Depatment of Information Technolgy, Vishwakarma Institute of Technology, Pune, India.Skin cancer stands as one of the prevalent and life-threatening malignancies, witnessing a substantial global surge in reported cases. Failure to diagnose it at its incipient stages may lead to metastasis, significantly elevating mortality rates. Early detection, however, presents a curative prospect. Thus, the prompt and precise diagnosis of skin cancers remains a paramount focus in current research. Numerous machine learning techniques have been seamlessly woven into the fascinating world of computer-aided skin cancer diagnosis and figuring out if that blemish is a real troublemaker. Machine learning is like the Sherlock Holmes of artificial intelligence. It's got these brainy models and algorithms that not only soak up information but also play psychic by predicting stuff on brand new data it's never laid eyes on before. In contrast to the conventional biopsy method, which is both laborious and costly, machine learning algorithms offer a viable alternative for early detection, reducing the burden on specialists while concurrently augmenting the diagnostic accuracy of skin lesions. In this article we delve into a thorough exploration of cutting-edge machine learning methods that play a crucial role in identifying those sneaky signs of skin cancer. I took a deep dive into the pool of relevant studies, offering you an insider's look into the performance of the friendly neighborhood the k-nearest neighbor approach, the robust SVM, and the sophisticated CNN. In the review we Openly discussed the inherent limitations and quirks of each algorithm, affording them the spotlight they deserve.http://www.sciencedirect.com/science/article/pii/S2772906024000633Skin cancerMachine learningMelanomaArtificial Intelligence |
| spellingShingle | Viomesh Singh Kavita A. Sultanpure Harshwardhan Patil Frontier machine learning techniques for melanoma skin cancer identification and categorization: An in-Depth review Oral Oncology Reports Skin cancer Machine learning Melanoma Artificial Intelligence |
| title | Frontier machine learning techniques for melanoma skin cancer identification and categorization: An in-Depth review |
| title_full | Frontier machine learning techniques for melanoma skin cancer identification and categorization: An in-Depth review |
| title_fullStr | Frontier machine learning techniques for melanoma skin cancer identification and categorization: An in-Depth review |
| title_full_unstemmed | Frontier machine learning techniques for melanoma skin cancer identification and categorization: An in-Depth review |
| title_short | Frontier machine learning techniques for melanoma skin cancer identification and categorization: An in-Depth review |
| title_sort | frontier machine learning techniques for melanoma skin cancer identification and categorization an in depth review |
| topic | Skin cancer Machine learning Melanoma Artificial Intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S2772906024000633 |
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