Automated Cervical Cancer Detection Using Pap Smear Images

2014 ◽  
pp. 267-278 ◽  
Author(s):  
Payel Rudra Paul ◽  
Mrinal Kanti Bhowmik ◽  
Debotosh Bhattacharjee
Keyword(s):  
Cervical Cancer ◽  
Cancer Detection ◽  
Pap Smear

scholarly journals Cervical cytochemistry: a potential adjunct to cytomorphology for detection of abnormal cells in cervical cancer screening

2020 ◽  
Vol 10 (1) ◽  
pp. 1639-1644
Author(s):  
Indrani Krishnappa ◽  
Kalyani R. ◽  
Raja Parthiban ◽  
Abhishek Agrawal
Keyword(s):  
Cervical Cancer ◽  
Acid Phosphatase ◽  
Cancer Screening ◽  
Cervical Cancer Screening ◽  
Cancer Detection ◽  
Pap Smear ◽  
Pap Test ◽  
Low Grade ◽  
Smear Examination ◽  
Intraepithelial Lesion

Background: Pap smear examination has been universally used as an effective screening tool for early detection of cervical carcinoma. The aim of this study was to assess the utility of Cervical Acid Phosphatase staining as an adjunct to routine Pap smear testing to improvethe sensitivity and specificity of routine Pap smear examination for cervical cancer detection. Materials and Methods: Cervical smears were taken from patients attending the gynecology department and a few cervical cancer screening programmes. One set of slides were alcohol fixed and stained with rapid pap stain and another set of slides were fixed in a special fixative and stained with Cervical Acid Phosphatase -Pap stain. The nuclear features of these Cervical Acid Phosphatase stained dysplastic cells was studied on Pap stain to diagnose cervical intraepithelial lesion/ malignancy. Results: Out of 489 cases included in the study 6 cases were diagnosed with intraepithelial lesion/ malignancy. On Cervical Acid Phosphatase -Pap stain 2 of the cases diagnosed as inflammatory smears on pap stain showed Cervical Acid Phosphatase positivity and thus were re evaluated. Mild nuclear atypia was observed in the Cervical Acid Phosphatase positive cells and these cases were diagnosed as Low grade squamous intraepithelial lesion and later biopsy proven to be Cervical intraepithelial Neoplasia I. Therefore Cervical Acid Phosphatase -Pap test was 100% sensitive and specific for cervical cancer detection. Conclusions: With 100% sensitivity Cervical Acid Phosphatase -Pap test satisfies the criteria of an efficient screening test.

scholarly journals Cervical cancer detection method using an improved cellular neural network (CNN) algorithm

2019 ◽  
Vol 14 (1) ◽  
pp. 210 ◽  
Author(s):  
Azian Azamimi Abdullah ◽  
Aafion Fonetta Dickson Giong ◽  
Nik Adilah Hanin Zahri
Keyword(s):  
Neural Network ◽  
Cervical Cancer ◽  
Cancer Detection ◽  
Pap Smear ◽  
Cellular Neural Network ◽  
Pap Smear Test ◽  
Abnormal Cells ◽  
Computer Based ◽  
Low Sensitivity ◽  
Cancerous Cells

<span>Cervical cancer is the second most common in Malaysia and the fourth frequent cancer among women in worldwide.  Pap smear test is often ignored although it is actually useful, beneficial and essential as screening tool for cervical cancer. However, Pap smear images have low sensitivity as well as specificity. Therefore, it is difficult to determine whether the abnormal cells are cancerous or not. Recently, computer-based algorithms are widely used in cervical cancer screening. In this study, an improved cellular neural network (CNN) algorithm is proposed as the solution to detect the cancerous cells in real-time by undergoing the image processing of Pap smear images. A few templates are combined and modified to form an ideal CNN algorithm to detect the cancerous cells in total of 115 Pap smear images. A MATLAB based CNN is developed for an automated detection of cervix cancerous cells where the templates segmented the nucleus of the cells. From the simulation results, our proposed CNN algorithm can detect the cervix cancer cells automatically with more than 88% accuracy.</span>

Pap smear based cervical cancer detection using residual neural networks deep learning architecture

2021 ◽  
Author(s):  
Vijayanand Sellamuthu Palanisamy ◽  
Rajiv Kannan Athiappan ◽  
Thirugnanasambandan Nagalingam
Keyword(s):  
Neural Networks ◽  
Cervical Cancer ◽  
Deep Learning ◽  
Cancer Detection ◽  
Pap Smear
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