[Retracted] Ant Colony Optimization‐Enabled CNN Deep Learning Technique for Accurate Detection of Cervical Cancer

Abstract

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.

HO-SsNF: heap optimizer-based self-systematized neural fuzzy approach for cervical cancer classification using pap smear images

“…Kavitha R. et al. ( 27 ) developed a computerized Cervical Precancerous Lesion Classification system using Quantum Invasive Weed Optimization with Deep Learning (CPLC-QIWODL) based on biological Pap smear images. Their approach involved preprocessing images with Gabor filtering (GF) and employing the deep variational autoencoder (DVAE) algorithm for classification, achieving a maximum detection rate of 99.07%.…”

Cervical Cancer Screening: Artificial Intelligence Algorithm For Automatic Diagnostic Support

“…In their study, Kavitha et al (2023) utilized ant colony optimization-enabled CNN deep learning technique to extract features and subsequently employed three different algorithms, namely CNN, multi-layer perception (MLP), and artificial neural network, to classify cancerous and noncancerous cervical images. The CNN classifier showed the highest accuracy among the three algorithms with 95.2%.…”

“…Convolutional neural networks (CNNs) have become a prominent tool in medical imaging and have shown promising results in various applications, including, to mention but a few, cancer detection and screening (Sompawong et al, 2019;Kavitha et al, 2023;Chandran et al, 2021), heart anomalies (Baccouche et al, 2020;Martins et al, 2021), and TB diagnosis (Rajpurkar et al, 2020;Kant and Srivastava, 2018). In this research, we employed a deep-learning CNN to classify cervical cancerous and non-cancerous images, leveraging the power of this technology to improve the accuracy and speed of diagnosis.…”

Image Segmentation Technology Based on Ant Colony Algorithm

“…They hope to learn more about the efficiency of boundary smoothing in increasing the accuracy and visual quality of segmented images produced by ACO-based segmentation algorithms through experimental validation and comparative analysis [9]. In addition, they highlight potential difficulties, future research objectives, and realworld applications for this integrated methodology.…”