Predicting Cervical Cancer Using Deep Learning Mutation-based Atom Search Optimization (MASO) Algorithm

Worldwide, women are compressed by cervical cancer, which is a prevalent malignancy. This disease, which is currently the fourth leading cause of death for women, shows no symptoms when it first arises. Cells that cause cervical cancer multiply gradually at the cervix. If this cancer is detected ear...

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Bibliographic Details
Published in:	2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies pp. 1 - 6
Main Authors:	Mayuri, A V R, Selvekumar, Subash, K, Aeron, Anurag
Format:	Conference Proceeding
Language:	English
Published:	IEEE 22-03-2024
Subjects:	cervical cancer Convolutional neural networks Deep Convolutional Neural Network (DCNN) Deep learning Ethics Feature extraction Medical services Mutation-based Atom Search Optimization (MASO) Prediction algorithms Statistical analysis
Online Access:	Get full text
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Summary:	Worldwide, women are compressed by cervical cancer, which is a prevalent malignancy. This disease, which is currently the fourth leading cause of death for women, shows no symptoms when it first arises. Cells that cause cervical cancer multiply gradually at the cervix. If this cancer is detected early enough, treatment can be effective. Presently, it is difficult for medical workers to detect this kind of cancer before it spreads rapidly. This study used a number of machine learning classification algorithms with risk markers to predict cervical cancer. These study suggestions mutation-based Atom Search Optimization (MASO) with a Deep Convolutional Neural Network (DCNN) to offer a original method for automated diagnosis of uterine cervical cancer. Though DCNN is used to extract features from medical imaging data, MASO is used to increase the optimization of the diagnostic model. Composed, these technologies have the potential to rise cervical cancer diagnosis accuracy and efficiency, offering an automated approach for early detection and prompt intervention. Our proposed model established a strong 95% accuracy rate for generalization. This study explores the difficulties caused by missing values and class imbalance in the specific dataset with the goal of assisting medical professionals in the early detection of cervical cancer and improving treatment for people that are impacted by the illness.
DOI:	10.1109/TQCEBT59414.2024.10545246