Deep learning techniques for cervical cancer diagnosis based on pathology and colposcopy images
Cervical cancer is a prevalent disease affecting millions of women worldwide every year. It requires significant attention, as early detection during the precancerous stage provides an opportunity for a cure. The screening and diagnosis of cervical cancer rely on cytology and colposcopy methods. Dee...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-01-01
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| Series: | Informatics in Medicine Unlocked |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352914824000595 |
| _version_ | 1797198803766345728 |
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| author | Hannah Ahmadzadeh Sarhangi Dorsa Beigifard Elahe Farmani Hamidreza Bolhasani |
| author_facet | Hannah Ahmadzadeh Sarhangi Dorsa Beigifard Elahe Farmani Hamidreza Bolhasani |
| author_sort | Hannah Ahmadzadeh Sarhangi |
| collection | DOAJ |
| description | Cervical cancer is a prevalent disease affecting millions of women worldwide every year. It requires significant attention, as early detection during the precancerous stage provides an opportunity for a cure. The screening and diagnosis of cervical cancer rely on cytology and colposcopy methods. Deep learning, a promising technology in computer vision, has emerged as a potential solution to improve the accuracy and efficiency of cervical cancer screening compared to traditional clinical inspection methods that are prone to human error. This review article discusses cervical cancer and its screening processes, followed by the Deep Learning training process and the classification, segmentation, and detection tasks for cervical cancer diagnosis. Additionally, we explored the most common public datasets used in both cytology and colposcopy and highlighted the popular and most utilized architectures that researchers have applied to both cytology and colposcopy. We reviewed 24 selected practical papers in this study and summarized them. This article highlights the remarkable efficiency in enhancing the precision and speed of cervical cancer analysis by Deep Learning, bringing us closer to early diagnosis and saving lives. |
| first_indexed | 2024-04-24T07:05:40Z |
| format | Article |
| id | doaj.art-a55b7d57a1bb4bca8301fa049d175806 |
| institution | Directory Open Access Journal |
| issn | 2352-9148 |
| language | English |
| last_indexed | 2024-04-24T07:05:40Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Informatics in Medicine Unlocked |
| spelling | doaj.art-a55b7d57a1bb4bca8301fa049d1758062024-04-22T04:11:46ZengElsevierInformatics in Medicine Unlocked2352-91482024-01-0147101503Deep learning techniques for cervical cancer diagnosis based on pathology and colposcopy imagesHannah Ahmadzadeh Sarhangi0Dorsa Beigifard1Elahe Farmani2Hamidreza Bolhasani3Department of Computer Engineering, Islamic Azad University Science and Research Branch, Tehran, Iran; Corresponding author.Department of Computer Engineering, Islamic Azad University Science and Research Branch, Tehran, IranTehran University of Medical Sciences, Tehran, IranDepartment of Computer Engineering, Islamic Azad University Science and Research Branch, Tehran, IranCervical cancer is a prevalent disease affecting millions of women worldwide every year. It requires significant attention, as early detection during the precancerous stage provides an opportunity for a cure. The screening and diagnosis of cervical cancer rely on cytology and colposcopy methods. Deep learning, a promising technology in computer vision, has emerged as a potential solution to improve the accuracy and efficiency of cervical cancer screening compared to traditional clinical inspection methods that are prone to human error. This review article discusses cervical cancer and its screening processes, followed by the Deep Learning training process and the classification, segmentation, and detection tasks for cervical cancer diagnosis. Additionally, we explored the most common public datasets used in both cytology and colposcopy and highlighted the popular and most utilized architectures that researchers have applied to both cytology and colposcopy. We reviewed 24 selected practical papers in this study and summarized them. This article highlights the remarkable efficiency in enhancing the precision and speed of cervical cancer analysis by Deep Learning, bringing us closer to early diagnosis and saving lives.http://www.sciencedirect.com/science/article/pii/S2352914824000595Deep learningCervical cancerPathologyCytology imagesColposcopy imagesClassification |
| spellingShingle | Hannah Ahmadzadeh Sarhangi Dorsa Beigifard Elahe Farmani Hamidreza Bolhasani Deep learning techniques for cervical cancer diagnosis based on pathology and colposcopy images Informatics in Medicine Unlocked Deep learning Cervical cancer Pathology Cytology images Colposcopy images Classification |
| title | Deep learning techniques for cervical cancer diagnosis based on pathology and colposcopy images |
| title_full | Deep learning techniques for cervical cancer diagnosis based on pathology and colposcopy images |
| title_fullStr | Deep learning techniques for cervical cancer diagnosis based on pathology and colposcopy images |
| title_full_unstemmed | Deep learning techniques for cervical cancer diagnosis based on pathology and colposcopy images |
| title_short | Deep learning techniques for cervical cancer diagnosis based on pathology and colposcopy images |
| title_sort | deep learning techniques for cervical cancer diagnosis based on pathology and colposcopy images |
| topic | Deep learning Cervical cancer Pathology Cytology images Colposcopy images Classification |
| url | http://www.sciencedirect.com/science/article/pii/S2352914824000595 |
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