Deep dive into deep learning methods for cervical cancer detection and classification
Cervical cancer continues to pose a significant global health challenge, highlighting the urgent need for accurate and efficient diagnostic techniques. Recent progress in deep learning has demonstrated considerable potential in improving the detection and classification of cervical cancer. This revi...
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| Format: | Article |
| Language: | English |
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Via Medica
2025-01-01
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| Series: | Reports of Practical Oncology and Radiotherapy |
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| Online Access: | https://journals.viamedica.pl/rpor/article/view/106148 |
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| author | Pooja Patre Dipti Verma |
| author_facet | Pooja Patre Dipti Verma |
| author_sort | Pooja Patre |
| collection | DOAJ |
| description | Cervical cancer continues to pose a significant global health challenge, highlighting the urgent need for accurate and efficient diagnostic techniques. Recent progress in deep learning has demonstrated considerable potential in improving the detection and classification of cervical cancer. This review presents a thorough analysis of deep learning methods utilized for cervical cancer diagnosis, with an emphasis on critical approaches, evaluation metrics, and the ongoing challenges faced in the field. We explore various deep learning architectures, particularly convolutional neural networks (CNNs), and their applications in the segmentation and classification of cervical cytology images. Key performance indicators, such as accuracy, sensitivity, specificity, and the area under the curve (AUC), are reviewed to assess the effectiveness of these models. Despite advancements, challenges like limited annotated datasets, inconsistencies in medical imaging, and the demand for more resilient models remain. Strategies like data augmentation, transfer learning, and semi-supervised learning are examined as potential solutions. This review synthesizes current research to guide future studies and clinical implementations, ultimately advancing early detection and treatment of cervical cancer through cutting-edge deep learning technologies. |
| format | Article |
| id | doaj-art-39bac5f2725d4ed6ac0c0e813bf34c7c |
| institution | Kabale University |
| issn | 1507-1367 2083-4640 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Via Medica |
| record_format | Article |
| series | Reports of Practical Oncology and Radiotherapy |
| spelling | doaj-art-39bac5f2725d4ed6ac0c0e813bf34c7c2025-08-21T05:46:05ZengVia MedicaReports of Practical Oncology and Radiotherapy1507-13672083-46402025-01-0130310.5603/rpor.106148Deep dive into deep learning methods for cervical cancer detection and classificationPooja Patre0Dipti Verma1Computer Science and Engineering, Vishwavidyalaya Engineering College Ambikapur, Chhattisgarh, Ambikapur, IndiaUniversity Teaching Department, Chhattisgarh Swami Vivekanand Technical University, Bhilai, IndiaCervical cancer continues to pose a significant global health challenge, highlighting the urgent need for accurate and efficient diagnostic techniques. Recent progress in deep learning has demonstrated considerable potential in improving the detection and classification of cervical cancer. This review presents a thorough analysis of deep learning methods utilized for cervical cancer diagnosis, with an emphasis on critical approaches, evaluation metrics, and the ongoing challenges faced in the field. We explore various deep learning architectures, particularly convolutional neural networks (CNNs), and their applications in the segmentation and classification of cervical cytology images. Key performance indicators, such as accuracy, sensitivity, specificity, and the area under the curve (AUC), are reviewed to assess the effectiveness of these models. Despite advancements, challenges like limited annotated datasets, inconsistencies in medical imaging, and the demand for more resilient models remain. Strategies like data augmentation, transfer learning, and semi-supervised learning are examined as potential solutions. This review synthesizes current research to guide future studies and clinical implementations, ultimately advancing early detection and treatment of cervical cancer through cutting-edge deep learning technologies.https://journals.viamedica.pl/rpor/article/view/106148cervical cancermachine learningdeep learningsegmentation |
| spellingShingle | Pooja Patre Dipti Verma Deep dive into deep learning methods for cervical cancer detection and classification Reports of Practical Oncology and Radiotherapy cervical cancer machine learning deep learning segmentation |
| title | Deep dive into deep learning methods for cervical cancer detection and classification |
| title_full | Deep dive into deep learning methods for cervical cancer detection and classification |
| title_fullStr | Deep dive into deep learning methods for cervical cancer detection and classification |
| title_full_unstemmed | Deep dive into deep learning methods for cervical cancer detection and classification |
| title_short | Deep dive into deep learning methods for cervical cancer detection and classification |
| title_sort | deep dive into deep learning methods for cervical cancer detection and classification |
| topic | cervical cancer machine learning deep learning segmentation |
| url | https://journals.viamedica.pl/rpor/article/view/106148 |
| work_keys_str_mv | AT poojapatre deepdiveintodeeplearningmethodsforcervicalcancerdetectionandclassification AT diptiverma deepdiveintodeeplearningmethodsforcervicalcancerdetectionandclassification |