Journal of Advanced Research in Applied Sciences and Engineering Technology · 2022 · 14 citations · 32 references
MedicineOncologyPathologyCellular PathologyCytopathologyGynecologic OncologyCancer DetectionCervical CancerCervical CytologyCervical PathologyCervicovaginal CytologyGynecologic CytologyCervical Cancer HistopathologyCervical Lesion ClassificationComputer-aided CytologyHigh-grade Squamous Intraepithelial Lesion DetectionMicroscopyPap Smear CytologyPap StainingPap Test Cytology
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.
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Machine learning for assisting cervical cancer diagnosis: An ensemble approach
Jiayi Lu, Enmin Song, Ahmed Ghoneim et al. · Future Generation Computer Systems · 2020 · 217 citations
A Machine Learning Method for Classification of Cervical Cancer
Jesse Jeremiah Tanimu, Mohamed Hamada, Mohammed Hassan et al. · Electronics · 2022 · 112 citations · Full text