Deep Learning in Cervical Cancer Diagnosis: Architecture, Opportunities, and Open Research Challenges
Nowadays, deep learning (DL) is a popular tool used in various applications in different fields, including the medical domain. DL techniques can cope with several challenges, which are difficult to resolve via traditional artificial intelligence (AI) techniques. Cervical cancer (CC) is one of the le...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10013651/ |
| _version_ | 1828047289997852672 |
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| author | Nina Youneszade Mohsen Marjani Chong Pei Pei |
| author_facet | Nina Youneszade Mohsen Marjani Chong Pei Pei |
| author_sort | Nina Youneszade |
| collection | DOAJ |
| description | Nowadays, deep learning (DL) is a popular tool used in various applications in different fields, including the medical domain. DL techniques can cope with several challenges, which are difficult to resolve via traditional artificial intelligence (AI) techniques. Cervical cancer (CC) is one of the leading reasons for death in females and ranks second after breast cancer, with more than 700 mortalities daily. This number is estimated to be 400,000 annually by 2030. However, if the cancer is detected in the early and precancerous stages, it is completely curable. Pap smear and colposcopy are the most widely used screening methods for the detection of cervical cancer. But manual screening approach suffers from a high false rate due to human errors. To overcome this challenge, machine learning (ML) and DL-based computer-aided diagnostic (CAD) techniques are being extensively expanded to automatically segment and categorize cervical cytology and colposcopy images. These methods increase the accuracy of detecting different stages of cervical cancer. Hence, there is an increased interest in creating computer-aided solutions for CC screening, especially in less-developed countries where the majority of cervical cancer-related fatalities occur. This review overviews state-of-the-art approaches that use DL techniques to analyze cervical cytology and screening images. It reviews and discusses relevant DL techniques, their architectures, classification methods, and the segmentation of cervical cytology and colposcopy images. Finally, it reviews the DL algorithms that are currently used in CC screening and offers useful insights, research opportunities and future directions in this field. |
| first_indexed | 2024-04-10T18:39:03Z |
| format | Article |
| id | doaj.art-e3baa49d013d423d99cd82188e12596a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T18:39:03Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e3baa49d013d423d99cd82188e12596a2023-02-02T00:00:16ZengIEEEIEEE Access2169-35362023-01-01116133614910.1109/ACCESS.2023.323583310013651Deep Learning in Cervical Cancer Diagnosis: Architecture, Opportunities, and Open Research ChallengesNina Youneszade0https://orcid.org/0000-0002-7478-1861Mohsen Marjani1https://orcid.org/0000-0002-7445-7332Chong Pei Pei2Faculty of Innovation and Technology, School of Computer Science (SCS), Taylor’s University, Subang Jaya, MalaysiaSchool of Biosciences, Taylor’s University, Subang Jaya, MalaysiaSchool of Biosciences, Taylor’s University, Subang Jaya, MalaysiaNowadays, deep learning (DL) is a popular tool used in various applications in different fields, including the medical domain. DL techniques can cope with several challenges, which are difficult to resolve via traditional artificial intelligence (AI) techniques. Cervical cancer (CC) is one of the leading reasons for death in females and ranks second after breast cancer, with more than 700 mortalities daily. This number is estimated to be 400,000 annually by 2030. However, if the cancer is detected in the early and precancerous stages, it is completely curable. Pap smear and colposcopy are the most widely used screening methods for the detection of cervical cancer. But manual screening approach suffers from a high false rate due to human errors. To overcome this challenge, machine learning (ML) and DL-based computer-aided diagnostic (CAD) techniques are being extensively expanded to automatically segment and categorize cervical cytology and colposcopy images. These methods increase the accuracy of detecting different stages of cervical cancer. Hence, there is an increased interest in creating computer-aided solutions for CC screening, especially in less-developed countries where the majority of cervical cancer-related fatalities occur. This review overviews state-of-the-art approaches that use DL techniques to analyze cervical cytology and screening images. It reviews and discusses relevant DL techniques, their architectures, classification methods, and the segmentation of cervical cytology and colposcopy images. Finally, it reviews the DL algorithms that are currently used in CC screening and offers useful insights, research opportunities and future directions in this field.https://ieeexplore.ieee.org/document/10013651/Deep learningclassificationcervical cancercolposcopy imagescytology images |
| spellingShingle | Nina Youneszade Mohsen Marjani Chong Pei Pei Deep Learning in Cervical Cancer Diagnosis: Architecture, Opportunities, and Open Research Challenges IEEE Access Deep learning classification cervical cancer colposcopy images cytology images |
| title | Deep Learning in Cervical Cancer Diagnosis: Architecture, Opportunities, and Open Research Challenges |
| title_full | Deep Learning in Cervical Cancer Diagnosis: Architecture, Opportunities, and Open Research Challenges |
| title_fullStr | Deep Learning in Cervical Cancer Diagnosis: Architecture, Opportunities, and Open Research Challenges |
| title_full_unstemmed | Deep Learning in Cervical Cancer Diagnosis: Architecture, Opportunities, and Open Research Challenges |
| title_short | Deep Learning in Cervical Cancer Diagnosis: Architecture, Opportunities, and Open Research Challenges |
| title_sort | deep learning in cervical cancer diagnosis architecture opportunities and open research challenges |
| topic | Deep learning classification cervical cancer colposcopy images cytology images |
| url | https://ieeexplore.ieee.org/document/10013651/ |
| work_keys_str_mv | AT ninayouneszade deeplearningincervicalcancerdiagnosisarchitectureopportunitiesandopenresearchchallenges AT mohsenmarjani deeplearningincervicalcancerdiagnosisarchitectureopportunitiesandopenresearchchallenges AT chongpeipei deeplearningincervicalcancerdiagnosisarchitectureopportunitiesandopenresearchchallenges |