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Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic).In: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018), 168-172.IEEE, 2018.","DOI":"10.1109\/ISBI.2018.8363547"},{"key":"10.1016\/j.critrevonc.2024.104528_bib46","unstructured":"N. Codella, V. Rotemberg, P. Tschandl, M.E. Celebi, S. Dusza, D. Gutman, B. Helba, A. Kalloo, K. Liopyris, M. Marchetti, et al. Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic).arXiv preprint arXiv:1902.03368, 2019."},{"issue":"1","key":"10.1016\/j.critrevonc.2024.104528_bib47","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A coefficient of agreement for nominal scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. 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Inform."},{"key":"10.1016\/j.critrevonc.2024.104528_bib114","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104398","article-title":"A review on recent developments in cancer detection using machine learning and deep learning models","volume":"80","author":"Maurya","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.critrevonc.2024.104528_bib115","doi-asserted-by":"crossref","first-page":"135175","DOI":"10.1109\/ACCESS.2023.3337032","article-title":"Improved bald eagle search optimization with deep learning-based cervical cancer detection and classification","volume":"11","author":"Mazroa","year":"2023","journal-title":"IEEE Access"},{"issue":"12","key":"10.1016\/j.critrevonc.2024.104528_bib116","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1097\/00000478-198812000-00001","article-title":"Zonal distribution of prostatic adenocarcinoma: correlation with histologic pattern and direction of spread","volume":"12","author":"McNeal","year":"1988","journal-title":"Am. J. Surg. Pathol."},{"key":"10.1016\/j.critrevonc.2024.104528_bib117","doi-asserted-by":"crossref","unstructured":"T. Mendon\u00e7a, P.M. Ferreira, J.S. Marques, A.R.S. Marcal, and J. Rozeira.Ph2 - a dermoscopic image database for research and benchmarking.In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 5437-5440, 2013.","DOI":"10.1109\/EMBC.2013.6610779"},{"issue":"17","key":"10.1016\/j.critrevonc.2024.104528_bib118","doi-asserted-by":"crossref","first-page":"7614","DOI":"10.3390\/s23177614","article-title":"Explainable convolutional neural networks for brain cancer detection and localisation","volume":"23","author":"Mercaldo","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.critrevonc.2024.104528_bib119","article-title":"Mfeuslnet: Skin cancer detection and classification using integrated ai with multilevel feature extraction-based unsupervised learning","volume":"51","author":"Midasala","year":"2024","journal-title":"Eng. Sci. Technol., Int. 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National Institute of Dental and Craniofacial Research, \u3008https:\/\/www.nidcr.nih.gov\/research\/data-statistics\/oral-cancer\/incidence\u3009.2024.Accessed: 05-07-2024."},{"key":"10.1016\/j.critrevonc.2024.104528_bib135","unstructured":"K. O\u2019shea, R. Nash.An introduction to convolutional neural networks.arXiv preprint arXiv:1511.08458, 2015."},{"issue":"14","key":"10.1016\/j.critrevonc.2024.104528_bib136","doi-asserted-by":"crossref","first-page":"5429","DOI":"10.3390\/s22145429","article-title":"Automatic detection of liver cancer using hybrid pre-trained models","volume":"22","author":"Othman","year":"2022","journal-title":"Sensors"},{"key":"10.1016\/j.critrevonc.2024.104528_bib137","unstructured":"Ovarian, fallopian tube, and primary peritoneal cancer. National Cancer Institute, \u3008https:\/\/www.cancer.gov\/types\/ovarian\u3009.2024.Accessed: 06-07-2024."},{"key":"10.1016\/j.critrevonc.2024.104528_bib138","doi-asserted-by":"crossref","unstructured":"P. Paayas and R. Annamalai.Ocean - ovarian cancer subtype classification and outlier detection using densenet121.In: 2023 Seventh International Conference on Image Information Processing (ICIIP), 827-831, 2023.","DOI":"10.1109\/ICIIP61524.2023.10537795"},{"issue":"7","key":"10.1016\/j.critrevonc.2024.104528_bib139","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1007\/s40042-024-01020-y","article-title":"Performance evaluation of improved median-modified wiener filter with segmentation method to improve resolution in computed tomographic images","volume":"84","author":"Park","year":"2024","journal-title":"J. Korean Phys. Soc."},{"issue":"7","key":"10.1016\/j.critrevonc.2024.104528_bib140","doi-asserted-by":"crossref","first-page":"1817","DOI":"10.1109\/TMI.2021.3066295","article-title":"Detection of prostate cancer in whole-slide images through end-to-end training with image-level labels","volume":"40","author":"Pinckaers","year":"2021","journal-title":"IEEE Trans. Med. 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