Detection of cervical cells based on improved SSD network

Jia, Dongfang; Zhou, Jialin; Zhang, Chuanwang

doi:10.1007/s11042-021-11015-7

Cited by 28 publications

(13 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…In fact, reading with AI assistance was approximately 380 times faster than reading by a typical pathologist. AI algorithms were able to distinguish between normal and cancerous Pap smears with an accuracy of 80–100% [ 68 , 110 , 111 , 113 , 115 , 116 , 119 , 120 , 125 , 127 , 130 , 131 , 135 , 137 ]. The application of AI in cytology for the detection of cervical cancer is shown in Table 3 .…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

show abstract

“…This issue was finally resolved by employing a more sophisticated backbone topology, such as ResNet, and making additional adjustments. SSD analysis was formerly used to detect cervical cancer cells [96]. The SSD network incorporates both positive and negative characteristics to counteract the insensitivity to small objects.…”

Section: ) Ssdmentioning

confidence: 99%

Deep Learning for the Detection of Acute Lymphoblastic Leukemia Subtypes on Microscopic Images: A Systematic Literature Review

2023

View full text Add to dashboard Cite

Computer vision research in detecting and classifying the subtype Acute Lymphoblastic Leukemia (ALL) has contributed to computer-aided diagnosis with improved accuracy. Another contribution is to serve as an assistant and second opinion for doctors and hematologists in diagnosing the ALL subtype.Early detection can also rely on computer-aided diagnosis to determine initial treatment. The purpose of this study is to review the progress of research in the detection and classification of ALL subtypes. The method's discussion focuses on the application of deep learning to the domain of object detection and classification. Motivations, challenges, and future research recommendations are thoroughly discussed to improve understanding and progress in this field of study. The study was carried out methodically by analyzing a collection of papers on the detection and classification of ALL subtypes published in science direct, IEEE, and PubMed from 2018 to 2022. The analysis of this paper field is included in the results of the selected paper. The paper selection from among 65 papers was based on inclusion and exclusion methods. Based on research methods and objectives, papers are divided into two large groups. The first group discusses the classification of ALL subtypes, while the second group discusses the detection of ALL subtypes. The discussion of prior research reveals some challenging issues and future work, such as the limited availability of the ALL subtypes dataset, the high computational complexity of the deep learning model, and further exploration of transformers in computer vision as a reference for research gaps that can contribute to future research.

show abstract

“…Cervical cytopathology image classification is an important method for diagnosing cervical cancer [ 18 ]. Moreover, previous studies have mentioned that cervical cell classification has important clinical consequences in cervical cancer screening at an early stage [ 19 , 20 ]. The effective classification of Pap smear cell images may be used to create automated and precise cervical cancer classification systems for early diagnosis [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Pap Smear Images Classification Using Machine Learning: A Literature Matrix

et al. 2022

View full text Add to dashboard Cite

Cervical cancer is regularly diagnosed in women all over the world. This cancer is the seventh most frequent cancer globally and the fourth most prevalent cancer among women. Automated and higher accuracy of cervical cancer classification methods are needed for the early diagnosis of cancer. In addition, this study has proved that routine Pap smears could enhance clinical outcomes by facilitating the early diagnosis of cervical cancer. Liquid-based cytology (LBC)/Pap smears for advanced cervical screening is a highly effective precancerous cell detection technology based on cell image analysis, where cells are classed as normal or abnormal. Computer-aided systems in medical imaging have benefited greatly from extraordinary developments in artificial intelligence (AI) technology. However, resource and computational cost constraints prevent the widespread use of AI-based automation-assisted cervical cancer screening systems. Hence, this paper reviewed the related studies that have been done by previous researchers related to the automation of cervical cancer classification based on machine learning. The objective of this study is to systematically review and analyses the current research on the classification of the cervical using machine learning. The literature that has been reviewed is indexed by Scopus and Web of Science. As a result, for the published paper access until October 2022, this study assessed past approaches for cervical cell classification based on machine learning applications.

show abstract

Detection of cervical cells based on improved SSD network

Cited by 28 publications

References 37 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

Deep Learning for the Detection of Acute Lymphoblastic Leukemia Subtypes on Microscopic Images: A Systematic Literature Review

Pap Smear Images Classification Using Machine Learning: A Literature Matrix

Contact Info

Product

Resources

About