Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancer
Abstract: Cervical cancer is one of the most dangerous and widespread illnesses afflicting women throughout the globe, particularly in East Africa and South Asia. In industrialised nations, the incidence of cervical cancer has consistently decreased over the past few decades. However, in developing countries, the reduction in incidence has been considerably slower, and in some instances, the incidence has increased. Implementing routine screenings for cervical cancer is something that has to be done to protect the health of women. Cervical cancer is famously difficult to diagnose and cure due to the slow rate at which it spreads and develops into more advanced stages of the disease. Screening for cervical cancer using a Pap smear, more often referred to as a Pap test, has the potential to detect the illness in its earlier stages. For the purpose of selecting features for this article, a gray level co-occurrence matrix (GLCM) technique was used. Following this step, classification is performed with methods such as convolutional neural network (CNN), support vector machine, and auto encoder. According to the findings of this experiment, the GLCM-CNN classifier proved to be the one with the highest degree of precision.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancer ; volume:18 ; number:1 ; year:2023 ; extent:10
Open life sciences ; 18, Heft 1 (2023) (gesamt 10)
- Creator
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Sudhakar, K.
Saravanan, D.
Hariharan, G.
Sanaj, M. S.
Kumar, Santosh
Shaik, Maznu
Gonzales, Jose Luis Arias
Aurangzeb, Khursheed
- DOI
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10.1515/biol-2022-0770
- URN
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urn:nbn:de:101:1-2023113013301477156797
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:25 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Sudhakar, K.
- Saravanan, D.
- Hariharan, G.
- Sanaj, M. S.
- Kumar, Santosh
- Shaik, Maznu
- Gonzales, Jose Luis Arias
- Aurangzeb, Khursheed
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