Systematic Review of Deep Learning Techniques in Skin Cancer Detection
Skin cancer is a serious health condition, as it can locally evolve into disfiguring states or metastasize to different tissues. Early detection of this disease is critical because it increases the effectiveness of treatment, which contributes to improved patient prognosis and reduced healthcare cos...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | BioMedInformatics |
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| Online Access: | https://www.mdpi.com/2673-7426/4/4/121 |
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| author | Carolina Magalhaes Joaquim Mendes Ricardo Vardasca |
| author_facet | Carolina Magalhaes Joaquim Mendes Ricardo Vardasca |
| author_sort | Carolina Magalhaes |
| collection | DOAJ |
| description | Skin cancer is a serious health condition, as it can locally evolve into disfiguring states or metastasize to different tissues. Early detection of this disease is critical because it increases the effectiveness of treatment, which contributes to improved patient prognosis and reduced healthcare costs. Visual assessment and histopathological examination are the gold standards for diagnosing these types of lesions. Nevertheless, these processes are strongly dependent on dermatologists’ experience, with excision advised only when cancer is suspected by a physician. Multiple approaches have surfed over the last few years, particularly those based on deep learning (DL) strategies, with the goal of assisting medical professionals in the diagnosis process and ultimately diminishing diagnostic uncertainty. This systematic review focused on the analysis of relevant studies based on DL applications for skin cancer diagnosis. The qualitative assessment included 164 records relevant to the topic. The AlexNet, ResNet-50, VGG-16, and GoogLeNet architectures are considered the top choices for obtaining the best classification results, and multiclassification approaches are the current trend. Public databases are considered key elements in this area and should be maintained and improved to facilitate scientific research. |
| format | Article |
| id | doaj-art-d60b8de5c3ce45c396081d0b54f3e7d8 |
| institution | Kabale University |
| issn | 2673-7426 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | BioMedInformatics |
| spelling | doaj-art-d60b8de5c3ce45c396081d0b54f3e7d82024-12-27T14:13:19ZengMDPI AGBioMedInformatics2673-74262024-11-01442251227010.3390/biomedinformatics4040121Systematic Review of Deep Learning Techniques in Skin Cancer DetectionCarolina Magalhaes0Joaquim Mendes1Ricardo Vardasca2Faculdade de Engenharia, Universidade do Porto, 4200-465 Porto, PortugalFaculdade de Engenharia, Universidade do Porto, 4200-465 Porto, PortugalInstituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, 4200-465 Porto, PortugalSkin cancer is a serious health condition, as it can locally evolve into disfiguring states or metastasize to different tissues. Early detection of this disease is critical because it increases the effectiveness of treatment, which contributes to improved patient prognosis and reduced healthcare costs. Visual assessment and histopathological examination are the gold standards for diagnosing these types of lesions. Nevertheless, these processes are strongly dependent on dermatologists’ experience, with excision advised only when cancer is suspected by a physician. Multiple approaches have surfed over the last few years, particularly those based on deep learning (DL) strategies, with the goal of assisting medical professionals in the diagnosis process and ultimately diminishing diagnostic uncertainty. This systematic review focused on the analysis of relevant studies based on DL applications for skin cancer diagnosis. The qualitative assessment included 164 records relevant to the topic. The AlexNet, ResNet-50, VGG-16, and GoogLeNet architectures are considered the top choices for obtaining the best classification results, and multiclassification approaches are the current trend. Public databases are considered key elements in this area and should be maintained and improved to facilitate scientific research.https://www.mdpi.com/2673-7426/4/4/121classificationdiagnosisdeep learningskin cancer |
| spellingShingle | Carolina Magalhaes Joaquim Mendes Ricardo Vardasca Systematic Review of Deep Learning Techniques in Skin Cancer Detection BioMedInformatics classification diagnosis deep learning skin cancer |
| title | Systematic Review of Deep Learning Techniques in Skin Cancer Detection |
| title_full | Systematic Review of Deep Learning Techniques in Skin Cancer Detection |
| title_fullStr | Systematic Review of Deep Learning Techniques in Skin Cancer Detection |
| title_full_unstemmed | Systematic Review of Deep Learning Techniques in Skin Cancer Detection |
| title_short | Systematic Review of Deep Learning Techniques in Skin Cancer Detection |
| title_sort | systematic review of deep learning techniques in skin cancer detection |
| topic | classification diagnosis deep learning skin cancer |
| url | https://www.mdpi.com/2673-7426/4/4/121 |
| work_keys_str_mv | AT carolinamagalhaes systematicreviewofdeeplearningtechniquesinskincancerdetection AT joaquimmendes systematicreviewofdeeplearningtechniquesinskincancerdetection AT ricardovardasca systematicreviewofdeeplearningtechniquesinskincancerdetection |