A Retrospective Study on Cervical Cancer Screening Using Pap Smear and Related Factors Among Women Living in Tiaret, Algeria

Chikhaoui, Mira; Smail, Fadhéla; Aissa, Ahlem Ben Sidi; Benhamida, Halima; Hamri, Radia

doi:10.1007/s40944-020-00471-y

Cited by 3 publications

(1 citation statement)

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“…In another study Chikhaoui et al, as a solution, the Cellular Neural Network algorithm is suggested to identify cancer cells in real-time by performing Pap smear image processing. The Pap smear procedure is essentially a microscopical method for examining the cells of the cervix for the existence of malignant or precancerous cells [21]. Numerous patterns are compounded and modified to create a perfect CNN algorithm for detecting cancer cells with 115 Pap smear images in total.…”

Section: E Organization Of the Articlementioning

confidence: 99%

Deep Learning in Cervical Cancer Diagnosis: Architecture, Opportunities, and Open Research Challenges

2023

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Nowadays, deep learning (DL) is a popular tool used in various applications in different fields, including the medical domain. DL techniques can cope with several challenges, which are difficult to resolve via traditional artificial intelligence (AI) techniques. Cervical cancer (CC) is one of the leading reasons for death in females and ranks second after breast cancer, with more than 700 mortalities daily. This number is estimated to be 400,000 annually by 2030. However, if the cancer is detected in the early and precancerous stages, it is completely curable. Pap smear and colposcopy are the most widely used screening methods for the detection of cervical cancer. But manual screening approach suffers from a high false rate due to human errors. To overcome this challenge, machine learning (ML) and DL-based computer-aided diagnostic (CAD) techniques are being extensively expanded to automatically segment and categorize cervical cytology and colposcopy images. These methods increase the accuracy of detecting different stages of cervical cancer. Hence, there is an increased interest in creating computer-aided solutions for CC screening, especially in less-developed countries where the majority of cervical cancer-related fatalities occur. This review overviews state-of-the-art approaches that use DL techniques to analyze cervical cytology and screening images. It reviews and discusses relevant DL techniques, their architectures, classification methods, and the segmentation of cervical cytology and colposcopy images. Finally, it reviews the DL algorithms that are currently used in CC screening and offers useful insights, research opportunities and future directions in this field

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Section: E Organization Of the Articlementioning

confidence: 99%