EconPapers    
Economics at your fingertips  
 

Classification of normal and abnormal overlapped squamous cells in pap smear image

T. P. Deepa and A. Nagaraja Rao ()
Additional contact information
T. P. Deepa: VIT Deemed-to-be-University
A. Nagaraja Rao: VIT Deemed-to-be-University

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 1, No 44, 519-531

Abstract: Abstract Cervical cancer is considered the second major cancer and a deadly disease found in the uterine cervix of sexually active women which is possible to treat if found at an early stage using a pre-screening procedure. One such procedure is Pap smear test which helps to find abnormal cervical cells which may lead to cancer. The manual analysis of these Pap smear cell samples is challenging and is even more complex when cells are overlapped which demands the need for an automated system to reduce the complexity. The software-based automated system can be implemented using image processing techniques which can be installed on any computer, making it easy to use, and low cost. The main objective of this paper is to classify the overlapped cells which are pre-segmented using Mid-point segmentation algorithm. The Convolutional Neural Network (CNN) model with Rectified Linear Unit (ReLU) classifier classifies the given input images into two classes - normal and abnormal. The paper concentrates on the performance comparison between proposed method to other works and also with the manual prediction done by a cytotechnician. The model uses 917 pap smear cell images from Herlev Dataset for training and testing. The proposed model is evaluated on performance measures like precision, recall, F-score, support. The results reflect that the proposed model is best suited for Pap smear test analysis with 96% accuracy. Hence, proposed work makes a useful assistive tool for radiologists and clinicians to detect cervical cell abnormalities from pap smear cytology images.

Keywords: Squamous; Overlapped cells; Pap smear; Cervical cancer; Neural network (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-022-01805-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:15:y:2024:i:1:d:10.1007_s13198-022-01805-z

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-022-01805-z

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-04-12
Handle: RePEc:spr:ijsaem:v:15:y:2024:i:1:d:10.1007_s13198-022-01805-z