Detection of cervical cancer cells in complex situation based on improved YOLOv3 network

Jia, Dongfang; He, Zihao; Zhang, Chuanwang; Yin, Wanting; Wu, Nengkai; Li, Ziqi

doi:10.1007/s11042-022-11954-9

Cited by 27 publications

(14 citation statements)

References 45 publications

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“…Computer-assisted diagnosis is believed to ease this situation because it can potentially lower the misdiagnosis rate and also reduce the workload of cytologists [ 100 ]. Therefore, several studies have addressed the subject of automatic cervical cancer diagnosis [ 64 , 65 , 66 , 67 , 68 , 74 , 75 , 80 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 ]. The investigations showed that AI-assisted methods were promising, and achieved a high sensitivity and specificity in clinical cervical cytological screening [ 66 ,…”

Section: Resultsmentioning

confidence: 99%

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

et al. 2022

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Objective: The likelihood of timely treatment for cervical cancer increases with timely detection of abnormal cervical cells. Automated methods of detecting abnormal cervical cells were established because manual identification requires skilled pathologists and is time consuming and prone to error. The purpose of this systematic review is to evaluate the diagnostic performance of artificial intelligence (AI) technologies for the prediction, screening, and diagnosis of cervical cancer and pre-cancerous lesions. Materials and Methods: Comprehensive searches were performed on three databases: Medline, Web of Science Core Collection (Indexes = SCI-EXPANDED, SSCI, A & HCI Timespan) and Scopus to find papers published until July 2022. Articles that applied any AI technique for the prediction, screening, and diagnosis of cervical cancer were included in the review. No time restriction was applied. Articles were searched, screened, incorporated, and analyzed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Results: The primary search yielded 2538 articles. After screening and evaluation of eligibility, 117 studies were incorporated in the review. AI techniques were found to play a significant role in screening systems for pre-cancerous and cancerous cervical lesions. The accuracy of the algorithms in predicting cervical cancer varied from 70% to 100%. AI techniques make a distinction between cancerous and normal Pap smears with 80–100% accuracy. AI is expected to serve as a practical tool for doctors in making accurate clinical diagnoses. The reported sensitivity and specificity of AI in colposcopy for the detection of CIN2+ were 71.9–98.22% and 51.8–96.2%, respectively. Conclusion: The present review highlights the acceptable performance of AI systems in the prediction, screening, or detection of cervical cancer and pre-cancerous lesions, especially when faced with a paucity of specialized centers or medical resources. In combination with human evaluation, AI could serve as a helpful tool in the interpretation of cervical smears or images.

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Section: Resultsmentioning

confidence: 99%

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

et al. 2022

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“… Name Dataset Algorithm Detection/Classification/Segmentation Results Tool Cell location [37] Herlev and Private Improved YOLOv3 Detection and Classification mAP of 78.87% No [36] Harlev and Private Trainable Weka Segmentation Detection Acc of 98.88% Yes [23] SIPaKMeD Faster R-CNN All mAP of 0.37798, and AR of 0.64 (5-class) No [24] Herlve and private YOLOv3 All mAP of 0.6 (10-class) No Our model SIPaKMeD and private Modified Mask-RCNN All mAP 0f 0.6 and mAR of 0.86 (5-class) Yes …”

Section: Resultsmentioning

confidence: 99%

Mask region-based CNNs for cervical cancer progression diagnosis on pap smear examinations

Rutili de Lima,

Khan,

Shah

et al. 2023

Heliyon

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“…This paper carried out substantial experiments to evaluate the proposed method and the experimental results validated the effectiveness of the proposed method, which achieved an mAP of 65.44% and gained a 5.7% increase in mAP together with an 18.5% increase in specificity. Jia et al studied one-stage detection method for cervical cancer cells carefully [95,96]. They improved SSD model by fusing feature maps between different layers in the first work.…”

Section: One-stage Supervised Learning Based Detectionmentioning

confidence: 99%

A Survey on Deep Learning-based Cervical Cytology Screening: from Cell Identification to Whole Slide Image Analysis

Jiang

Shen

et al. 2023

Preprint

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Cervical cancer is one of the most common cancers in daily life. Early detection and diagnosis can effectively help facilitate subsequent clinical treatment and management. With the growing advancement of artificial intelligence (AI) and deep learning (DL) techniques, an increasing number of computer-aided diagnosis (CAD) methods based on deep learning have been applied in cervical cytology screening. In this paper, we survey more than 70 publications since 2016 to provide a systematic and comprehensive review of DL-based cervical cytology screening. First, we provide a concise summary of the medical and biological knowledge pertaining to cervical cytology, since we hold a firm belief that a comprehensive biomedical understanding can significantly contribute to the development of CAD systems. Then, we collect a wide range of public cervical cytology datasets. Besides, image analysis approaches and applications including cervical cell identification, abnormal cell or area detection, cell region segmentation and cervical whole slide image diagnosis are summarized. Finally, we discuss the present obstacles and promising directions for future researches in automated cervical cytology screening.

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Detection of cervical cancer cells in complex situation based on improved YOLOv3 network

Cited by 27 publications

References 45 publications

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

Mask region-based CNNs for cervical cancer progression diagnosis on pap smear examinations

A Survey on Deep Learning-based Cervical Cytology Screening: from Cell Identification to Whole Slide Image Analysis

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