Performance of a convolutional neural network in screening liquid based cervical cytology smears.
Context: Cervical cancer is the second most common cancer in women. The liquid based cervical cytology (LBCC) is a useful tool of choice for screening cervical cancer. Aims: To train a convolutional neural network (CNN) to identify abnormal foci from LBCC smears. Settings and Design: We have chosen...
| Publicado en: | Journal of Cytology Vol. 36; no. 3; pp. 146 - 152 |
|---|---|
| Autores principales: | , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Wolters Kluwer India Pvt Ltd
Jul-Sep2019
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137096076&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137096076 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09709371 58DU jtl: Journal of Cytology issn: 09709371 maglogo: N pubinfo: dt: Jul-Sep2019 vid: 36 iid: 3 pid: 16919 pub: Wolters Kluwer India Pvt Ltd artinfo: ui: 137096076 137096076 137096076 10.4103/JOC.JOC_201_18 137096076 ppf: 146 ppct: 6 formats: tig: atl: Performance of a convolutional neural network in screening liquid based cervical cytology smears. aug: au: Sanyal, Parikshit Barui, Sanghita Deb, Prabal Sharma, Harish affil: Department of Pathology, Military Hospital Jalandhar Cantt, Punjab sug: subj: Neural Networks (Computer) Cervix Neoplasms Diagnosis Cervical Smears Methods Cancer Screening Methods Sensitivity and Specificity Human Retrospective Design Photography Neoplasm Grading Cervical Intraepithelial Neoplasia Diagnosis Chi Square Test for Contingency Tables Predictive Value of Tests False Positive Results Neutrophils Programming Languages ab: Context: Cervical cancer is the second most common cancer in women. The liquid based cervical cytology (LBCC) is a useful tool of choice for screening cervical cancer. Aims: To train a convolutional neural network (CNN) to identify abnormal foci from LBCC smears. Settings and Design: We have chosen retrospective study design from archived smears of patients undergoing screening from cervical cancer by LBCC smears. Materials and Methods: 2816 images, each of 256 × 256 pixels, were prepared from microphotographs of these LBCC smears, which included 816 "abnormal" foci (low grade or high grade squamous intraepithelial lesion) and 2000 'normal' foci (benign epithelial cells and reactive changes). The images were split into three sets, Training, Testing, and Evaluation. A convolutional neural network (CNN) was developed with the python programming language. The CNN was trained with the Training dataset; performance was assayed concurrently with the Testing dataset. Two CNN models were developed, after 20 and 10 epochs of training, respectively. The models were then run on the Evaluation dataset. Statistical Analysis Used: A contingency table was prepared from the original image labels and the labels predicted by the CNN. Results: Combined assessment of both models yielded a sensitivity of 95.63% in detecting abnormal foci, with 79.85% specificity. The negative predictive value was high (99.19%), suggesting potential utility in screening. False positives due to overlapping cells, neutrophils, and debris was the principal difficulty met during evaluation. Conclusions: The CNN shows promise as a screening tool; however, for its use in confirmatory diagnosis, further training with a more diverse dataset will be required. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|