Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancer
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,...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2023-11-01
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| Series: | Open Life Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/biol-2022-0770 |
| _version_ | 1797405103292940288 |
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| author | Sudhakar K. Saravanan D. Hariharan G. Sanaj M. S. Kumar Santosh Shaik Maznu Gonzales Jose Luis Arias Aurangzeb Khursheed |
| author_facet | Sudhakar K. Saravanan D. Hariharan G. Sanaj M. S. Kumar Santosh Shaik Maznu Gonzales Jose Luis Arias Aurangzeb Khursheed |
| author_sort | Sudhakar K. |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T03:05:52Z |
| format | Article |
| id | doaj.art-4f4f221a077c4f05aab25a9d90f81765 |
| institution | Directory Open Access Journal |
| issn | 2391-5412 |
| language | English |
| last_indexed | 2024-03-09T03:05:52Z |
| publishDate | 2023-11-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Life Sciences |
| spelling | doaj.art-4f4f221a077c4f05aab25a9d90f817652023-12-04T07:59:13ZengDe GruyterOpen Life Sciences2391-54122023-11-01181e191e20310.1515/biol-2022-0770Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancerSudhakar K.0Saravanan D.1Hariharan G.2Sanaj M. S.3Kumar Santosh4Shaik Maznu5Gonzales Jose Luis Arias6Aurangzeb Khursheed7Department of Computer Science & Engineering, Madanapalle Institute of Technology & Science, Madanapalle, Andhra Pradesh, IndiaDepartment of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, IndiaDepartment of Artificial Intelligence and Machine Learning, Malla Reddy University, Hyderabad, IndiaDepartment of Computer Science and Engineering, Adi Shankara Institute of Engineering and Technology, Kalady, Ernakulam, Kerala, IndiaDepartment of Computer Science, ERA University, Lucknow, Uttar Pradesh, IndiaDepartment of ECE, Vidya Jyothi institute of Technology, Aziznagar, Hyderabad, IndiaUniversidad Tecnologica de los Andes, Abancay, PeruDepartment of Computer Engineering, College of Computer and Information Sciences, King Saud University, P. O. Box 51178, Riyadh11543, Saudi ArabiaCervical 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.https://doi.org/10.1515/biol-2022-0770cervical cancer detectionconvolutional neural networkaccuracygray level co-occurrence matrixfeature selectionpep test images |
| spellingShingle | Sudhakar K. Saravanan D. Hariharan G. Sanaj M. S. Kumar Santosh Shaik Maznu Gonzales Jose Luis Arias Aurangzeb Khursheed Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancer Open Life Sciences cervical cancer detection convolutional neural network accuracy gray level co-occurrence matrix feature selection pep test images |
| title | Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancer |
| title_full | Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancer |
| title_fullStr | Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancer |
| title_full_unstemmed | Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancer |
| title_short | Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancer |
| title_sort | optimised feature selection driven convolutional neural network using gray level co occurrence matrix for detection of cervical cancer |
| topic | cervical cancer detection convolutional neural network accuracy gray level co-occurrence matrix feature selection pep test images |
| url | https://doi.org/10.1515/biol-2022-0770 |
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