Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancer

Author:

Sudhakar K.1,Saravanan D.2,Hariharan G.3,Sanaj M. S.4,Kumar Santosh5,Shaik Maznu6,Gonzales Jose Luis Arias7,Aurangzeb Khursheed8

Affiliation:

1. Department of Computer Science & Engineering, Madanapalle Institute of Technology & Science , Madanapalle , Andhra Pradesh , India

2. Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology , Chennai , Tamil Nadu , India

3. Department of Artificial Intelligence and Machine Learning, Malla Reddy University , Hyderabad , India

4. Department of Computer Science and Engineering, Adi Shankara Institute of Engineering and Technology, Kalady , Ernakulam , Kerala , India

5. Department of Computer Science, ERA University , Lucknow , Uttar Pradesh , India

6. Department of ECE, Vidya Jyothi institute of Technology , Aziznagar , Hyderabad , India

7. Universidad Tecnologica de los Andes , Abancay , Peru

8. Department of Computer Engineering, College of Computer and Information Sciences, King Saud University , P. O. Box 51178 , Riyadh 11543 , Saudi Arabia

Abstract

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.

Publisher

Walter de Gruyter GmbH

Subject

General Agricultural and Biological Sciences,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Neuroscience

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