Ant Colony Optimization-Enabled CNN Deep Learning Technique for Accurate Detection of Cervical Cancer

• Published in: BioMed research international Vol. 2023; no. 1; p. 1742891
• Main Authors: Kavitha, R., Jothi, D. Kiruba, Saravanan, K., Swain, Mahendra Pratap, Gonzáles, José Luis Arias, Bhardwaj, Rakhi Joshi, Adomako, Elijah
• Format: Journal Article
• Language: English
• Published: United States Hindawi 2023; John Wiley & Sons, Inc
• Subjects: Algorithms; Ant colony optimization; Anxiety; Artificial intelligence; Automation; Cancer; Cell death; Cell growth; Cell proliferation; Cervical cancer; Cervix; Deep Learning; Diagnosis; Ethics; Fatalities; Female; Fuzzy algorithms; Fuzzy logic; Fuzzy systems; Humans; Illnesses; Machine learning; Medical imaging; Mortality; Neural networks; Pap smear; Patients; Uterine cancer; Uterine Cervical Neoplasms - diagnosis; Uterus; Womens health; India
• ISSN: 2314-6133; 2314-6141; 2314-6141
• DOI: 10.1155/2023/1742891

Cancer is characterized by abnormal cell growth and proliferation, which are both diagnostic indicators of the disease. When cancerous cells enter one organ, there is a risk that they may spread to adjacent tissues and eventually to other organs. Cancer of the cervix of the uterus often initially manifests itself in the uterine cervix, which is located at the very bottom of the uterus. Both the growth and death of cervical cells are characteristic features of this condition. False-negative results provide a significant moral dilemma since they may cause women to get an incorrect diagnosis of cancer, which in turn can result in the woman’s premature death from the disease. False-positive results do not raise any significant ethical concerns; but they do require a patient to go through an expensive and time-consuming treatment process, and they also cause the patient to experience tension and anxiety that is not warranted. In order to detect cervical cancer in its earliest stages in women, a screening procedure known as a Pap test is often performed. This article describes a technique for improving images using Brightness Preserving Dynamic Fuzzy Histogram Equalization. To individual components and find the right area of interest, the fuzzy c-means approach is applied. The images are segmented using the fuzzy c-means method to find the right area of interest. The feature selection algorithm is the ACO algorithm. Following that, categorization is carried out utilizing the CNN, MLP, and ANN algorithms.