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Cervical Cancer Cell Detection Using Image Processing and Matlab
Subject area: Science,Engineering and Technology · Area of research: Medical College Project
Abstract
This work presents a unique automated technique for the identification of cervical cancer cells using image processing and MATLAB. The method accurately detects abnormal cervical cells by employing complex algorithms for segmentation, feature extraction, and image enhancement. The proposed method performs well in differentiating between normal and abnormal cells. This study might improve patient outcomes and early detection rates by making cervical cancer screening more effective. Cervical cancer ranks as the second most prevalent cancer in women of all ages. Because it has no symptoms, this cancer cannot be detected in its early stages. The main problem with this cancer is that it doesn't show any signs until it has progressed to an advanced stage. This is related to both the cancer and the scarcity of pathologists who can do cancer screenings. Cervical cancer screening is recommended for a number of reasons, including dread of the repercussions of cervical cancer, a sense of risk, the need for complete examination, diagnosis, and treatment of all illnesses to preserve good health, and the need to keep communication lines open with medical professionals. Ignorance is one of the biggest barriers to repeat screening. Two significant barriers are a lack of reminders and a poor comprehension of the importance of ongoing screening.
References
[1] Cervical cancer diagnosis using very deep networks over different activation functions (2021).
[2] Segmentation of pap smear images for cervical cancer detection (2020).
[3] Survey of cervical cancer prediction using Machine learning (2018).
[4] Cervical cancer diagnosis health care system using hybrid object detection Adversal Networks (2021).
[5] Automated Diagnosis and classification of Cervical cancer from pap smear images (2019).
[6] Adaptive pruning of transfer learned deep Convolutional Neural Network for classification of cervical cancer pap smear images (2020).
[7] A Survey for Cervical cytopathology image analysis using deep learning (2020).
[8] A fuzzy Reasoning model for cervical cancer neoplasia classification using temporal grayscale change and texture of cervical images during acetic acid tests (2019).
[9] Cervical cancer diagnostics Healthcare system using hybrid object detection adversarial networks (2022).
[10] Analysis of pixel intensity variation by performing morphological operations for image segmentation on cervical cancer pap smear images (2021).
[11] An Automatic segmentation of cervical intraepithelial neoplasia from parabasal cells (2014).
[12] Unsupervised segmentation of cervical cells images using gaussian Mixture model (2016).
[13] Exploring Contextual Relationships for abnormal cervical cell detection (2023).
[14] Development of cervix: Cervical cancer early response visual identification system (2019).
How to cite this paper
@article{1705361,
author = {Venkata Chandu K., Balanaga Sairam K., Yaswanth K., Surya Prakash J., Devi S.},
title = {Cervical Cancer Cell Detection Using Image Processing and Matlab},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {7},
number = {7},
pages = {88-92},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1705361.pdf},
abstract = {This work presents a unique automated technique for the identification of cervical cancer cells using image processing and MATLAB. The method accurately detects abnormal cervical cells by employing complex algorithms for segmentation, feature extraction, and image enhancement. The proposed method performs well in differentiating between normal and abnormal cells. This study might improve patient outcomes and early detection rates by making cervical cancer screening more effective. Cervical cancer ranks as the second most prevalent cancer in women of all ages. Because it has no symptoms, this cancer cannot be detected in its early stages. The main problem with this cancer is that it doesn't show any signs until it has progressed to an advanced stage. This is related to both the cancer and the scarcity of pathologists who can do cancer screenings. Cervical cancer screening is recommended for a number of reasons, including dread of the repercussions of cervical cancer, a sense of risk, the need for complete examination, diagnosis, and treatment of all illnesses to preserve good health, and the need to keep communication lines open with medical professionals. Ignorance is one of the biggest barriers to repeat screening. Two significant barriers are a lack of reminders and a poor comprehension of the importance of ongoing screening.},
month = {January},
}