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Kavitha, R.
- Computer Aided Diagnosis for Detection and Stage Identification of Cervical Cancer by Using Pap Smear Screening Test Images
Abstract Views :171 |
PDF Views:3
Authors
Affiliations
1 Department of Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, IN
2 Department of Computer Application, Sengamala Thayaar Educational Trust Women’s College, IN
3 Department of Computer Science and Engineering, PSN College of Engineering and Technology, IN
1 Department of Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, IN
2 Department of Computer Application, Sengamala Thayaar Educational Trust Women’s College, IN
3 Department of Computer Science and Engineering, PSN College of Engineering and Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 6, No 4 (2016), Pagination: 1244-1251Abstract
The majority of the women of the world were affected by the disease of cervical cancer. As a result of this disease, their death rate was increase as hasty level. Hence so many number of research people was focused this notion as their research interest and also they have done so many number of solutions for finding this cancer by using some image processing technique and achieved a good results only in advanced and high cost techniques of LBC, biopsy or Colposcopy test Images. Therefore the reason, the authors have chosen this problem and also did not only to find whether the patient is affected by a cancer or not. In addition to the patient was affected by this cancer means and also to identify which severity stage of this disease the patient could be live. Then this work has done in based on the images of low cost pap smear screening test by using various image processing techniques with the help of Computerized Image Processing Software Interactive Data Language (IDL-Image Processing Language). Thus the final reports would be very useful to the pathologists for further analysis.Keywords
Cancer, Cervical Cancer, Nuclei Segmentation, Feature Identification, Classification.
- Cervical Cancer Detection and Classification by using Effectual Integration of Directional Gabor Texture Feature Extraction and Hybrid Kernel Based Support Vector Classification
Abstract Views :197 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, IN
2 Department of Computer Applications, The MDT Hindu College, IN
3 Department of Computer Science and Engineering, Universal College of Engineering and Technology, IN
1 Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, IN
2 Department of Computer Applications, The MDT Hindu College, IN
3 Department of Computer Science and Engineering, Universal College of Engineering and Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 9, No 3 (2019), Pagination: 1935-1939Abstract
Planning of invigorating representation is a troublesome and testing process because of the unpredictability of the images and absence of models of the life systems that thoroughly catches the reasonable expressions in each structure. Cervical malignant growth is one of the noteworthy reasons for death among different kinds of the diseases in women around the world. Genuine and auspicious determination can keep the life to some dimension. Therefore, we have proposed a computerized dependable framework for the analysis of the cervical malignancy utilizing surface highlights and machine learning calculation in Pap smear images, it is extremely advantageous to anticipate disease, likewise expands the dependability of the determination. Proposed framework is a multi-organize framework for cell nucleus extraction and disease finding. To begin with, clamor expulsion is performed in the preprocessing venture on the Pap smear images. Exterior highlights are separated from these demand free Pap smear images. Next period of the proposed framework is classification that depends on these separated highlights, SVM classification is utilized. Over 94% exactness is accomplished by the classification stage, demonstrated that the proposed calculation precision is great at recognizing the disease in the Pap smear images.Keywords
Cervical Cancer, Feature Extraction, DGTF, Classification, Hybrid Kernel SVM.References
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