A Recent Systematic Review of Cervical Cancer Diagnosis: Detection and Classification

Author:

Wan Azani Mustafa ,Nur Ain Alias ,Mohd Aminuddin Jamlos ,Shahrina Ismail ,Hiam Alquran

Abstract

Women around the world are frequently diagnosed with cervical cancer. In the beginning, there were no symptoms for the fourth most common cause of fatality in women. Cells of cervical cancer develop gradually at the cervix. Several studies have mentioned that the initial detection of cervical tumours is essential for cancer to be properly treated and to make sure cancer can be successfully treated while minimizing deaths due to cervical cancer. The diagnosis of such cancer before it spreads fast is currently a pressing issue for healthcare professionals. This also provides an extensive understanding with respect to the physical characteristics of the healthy and unhealthy cervix and aids in early treatment planning by giving detailed information about one another. Utilizing image segmentation, several techniques are employed to find malignancy. The dataset contains four distinct pathological pictures, including normal, malignancy, and high-grade squamous intraepithelial lesions (HSIL). While pap tests are the most popular way to diagnose cervical cancer, their accuracy depends a lot on how well cytotechnicians can use brightfield microscopy to spot abnormal cells on smears.

Publisher

Akademia Baru Publishing

Subject

General Engineering

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Segmentation and Classification Techniques for Pap Smear Images in Detecting Cervical Cancer: A Systematic Review;IEEE Access;2024

2. Data Augmentation Techniques to Detect Cervical Cancer Using Deep Learning: A Systematic Review;Lecture Notes in Networks and Systems;2024

3. Beyond Reality: A Synthesis Analysis of Metaverse-Based Immersive Learning Experience;2023 3rd International Conference on Mobile Networks and Wireless Communications (ICMNWC);2023-12-04

4. Analysis Of Diagnostic Value of cervical Cancer Disease Through Artificial Intelligence Based System;Proceedings of the 5th International Conference on Information Management & Machine Intelligence;2023-11-23

5. Counting Non-Overlapping Abnormal Cervical Cells in Whole Slide Images;2023 6th International Conference on Engineering Technology and its Applications (IICETA);2023-07-15

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