Isfahan University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! On (X network) By
Diagnosis of Cervical Cancer Using Texture and Morphological Features in Pap Smear Images Publisher



Hosseinabadi H1 ; Mehridehnavi A1 ; Talebi A2 ; Momenzadeh M1 ; Vard A1
Authors
Show Affiliations
Authors Affiliations
  1. 1. Department of Bioelectric, School of Modern Technologies of Medical Sciences, Isfahan University of Medical Sciences, Isfahan, Iran
  2. 2. Department of Pathology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

Source: Journal of Isfahan Medical School Published:2020


Abstract

Background: Cervical cancer is one of the most common cancers among women worldwide, which can be diagnosed more quickly via using digital systems. The purpose of this study was to classify the cells in Pap smear test images into two types of normal and abnormal by using image processing to diagnose cervical cancers. Methods: We used Herlev public database, which contained 917 cells. 35 geometric and 263 histologic features such as Gray Level Co-Occurrence Matrix (GLCM), Local Binary Pattern (LBP), and rotational gradient histogram were extracted from cell images. T test filter method was applied on the data set after extraction of geometrical and textural features. We used different classification methods such as support vector machine (SVM), decision tree (DT), k nearest neighbor (KNN) and ensemble classifiers. Findings: The best results were for SVM classifier as 97.5% accuracy in two-class classification with 20 features. Conclusion: Feature selection and feature extraction methods are very important for classify normal and abnormal cervical cell images. By optimizing and choosing the right methods, we can optimizing accuracy, and speed and error (2-3 percent). © 2020 Isfahan University of Medical Sciences(IUMS). All rights reserved.
Related Docs
View other Related Docs
1. Ensemble Supervised Classification Method Using the Regions of Interest and Grey Level Co-Occurrence Matrices Features for Mammograms Data, Iranian Journal of Radiology (2015)
2. A Histopathological Image Dataset for Grading Breast Invasive Ductal Carcinomas, Informatics in Medicine Unlocked (2020)
3. Classification of Effective Cells in Diagnosis of Chronic Myeloid Leukemia (Cml) Using Semi-Automatic Image Processing of Microscopic Images, Journal of Isfahan Medical School (2017)
4. Automatic Detection of Acute Lymphoblastic Leukaemia Based on Extending the Multifractal Features, IET Image Processing (2020)
5. Differentiation Between Pancreatic Ductal Adenocarcinoma and Normal Pancreatic Tissue for Treatment Response Assessment Using Multi-Scale Texture Analysis of Ct Images, Journal of Biomedical Physics and Engineering (2022)
6. Computer Aided Detection and Classification of Acute Lymphoblastic Leukemia Cell Subtypes Based on Microscopic Image Analysis, Microscopy Research and Technique (2016)
7. Notice of Removal: Malignant Tumor Detection Using Linear Support Vector Machine in Breast Cancer Based on New Optimization Algorithms, Proceedings - 2012 International Symposium on Instrumentation and Measurement, Sensor Network and Automation, IMSNA 2012 (2012)
8. A Novel and More Efficient Approach for Automatic Diagnosis of Acute Lymphoblastic Leukemic Cells Based on Combining Geometrical and Statistical Features of Blood Cells, Journal of Isfahan Medical School (2017)
Experts (# of related papers)
View all Related Experts
Ardeshir Talebi (6)
Saeed Kermani (5)
Other Related Docs
9. A Novel Feature Ranking Method for Prediction of Cancer Stages Using Proteomics Data, PLoS ONE (2017)
10. A Hybrid Computer-Aided-Diagnosis System for Prediction of Breast Cancer Recurrence (Hpbcr) Using Optimized Ensemble Learning, Computational and Structural Biotechnology Journal (2017)
11. Screening of Covid-19 Based on the Extracted Radiomics Features From Chest Ct Images, Journal of X-Ray Science and Technology (2021)
12. Classification of Prostate Cancerous Tissues by Support Vector Machine Algorithm With Different Kernels From T2-Weighted Magnetic Resonance Images, Journal of Isfahan Medical School (2021)
13. Classification of Chronic Myeloid Leukemia Cell Subtypes Based on Microscopic Image Analysis, EXCLI Journal (2019)
14. Identification of Effective Factors in Breast Cancer Survival in Isfahan Using Machine Learning Techniques, Journal of Advances in Medical and Biomedical Research (2024)
15. Automatic Diagnosis of Vulvovaginal Candidiasis From Pap Smear Images, Journal of Microscopy (2017)
16. Comparison of Different Classifiers for Prediction of Breast Cancer Metastasis in Microarray Analysis, Journal of Isfahan Medical School (2014)
17. An Optimized Framework for Cancer Prediction Using Immunosignature, Journal of Medical Signals and Sensors (2018)
18. Classification and Staging of Brain Glioma Tumors Using Magnetic Resonance Imaging and Machine Learning Algorithms, Journal of Isfahan Medical School (2022)
19. Recognition of Acute Lymphoblastic Leukemia Cells in Microscopic Images Using K-Means Clustering and Support Vector Machine Classifier, Journal of Medical Signals and Sensors (2015)
20. Using Classification and K-Means Methods to Predict Breast Cancer Recurrence in Gene Expression Data, Journal of Medical Signals and Sensors (2022)
21. Plasmacell Cad: A Computer-Aided Diagnosis Software Tool for Plasma Cell Recognition and Characterization in Microscopic Images, International Journal of Medical Informatics (2025)