Cervical cancer is a prevalent and fatal disease that affects women all over the world. This affects roughly 0.5 million women annually and kills over 0.3 million people. Recently, a significant amount of literature has emerged around the advancement of technologies for identifying cervical cancer cells in women. Previously, diagnosing cervical cancer was done manually, which could lead to false positives or negatives. The best way of interpreting Pap smear images and automatically diagnose cervical cancer are still up for debate among the researchers. Method used in this study is the contrast enhancement technique for pre-processing and edge detection-based for segmentation of the nucleus. In this study, the average performance results of the method showed an accuracy of 96.99% in the seven-class problem using Herlev dataset. The present finding also support this study which concluded the results of accuracy achieved for the algorithm used for nucleus detection is improved by 6.15% when comparing to previous work. The accuracy value is in the lines of earlier literature that achieved accuracy of the approach used above 90% for seven class of cells. The major feature of the suggested approach is an improvement in the ability to anticipate which cells are aberrant and which are normal. Adding more classifiers could improve the suggested system even further. Therefore, a cervical cancer screening system might utilize this framework to identify women who have precancerous lesions.

Cervical cancer is a prevalent and deadly cancer that affects women all over the world. It affects about 0.5 million women anually and results in over 0.3 million fatalities. Diagnosis of this cancer was previously done manually, which could result in false positives or negatives. The researchers are still contemplating how to detect cervical cancer automatically and how to evaluate Pap smear images. Hence, this paper has reviewed several detection methods from the previous researches that has been done before. This paper reviews pre-processing, detection method framework for nucleus detection, and analysis performance of the method selected. There are four methods based on a reviewed technique from previous studies that have been running through the experimental procedure using Matlab, and the dataset used is established Herlev Dataset. The results show that the highest performance assessment metric values obtain from Method 1: Thresholding and Trace region boundaries in a binary image with the values of precision 1.0, sensitivity 98.77%, specificity 98.76%, accuracy 98.77% and PSNR 25.74% for a single type of cell. Meanwhile, the average values of precision were 0.99, sensitivity 90.71%, specificity 96.55%, accuracy 92.91% and PSNR 16.22%. The experimental results are then compared to the existing methods from previous studies. They show that the improvement method is able to detect the nucleus of the cell with higher performance assessment values. On the other hand, the majority of current approaches can be used with either a single or a large number of cervical cancer smear images. This study might persuade other researchers to recognize the value of some of the existing detection techniques and offer a strong approach for developing and implementing new solutions.

Cervical cancer is regularly diagnosed in women all over the world. This cancer is the seventh most frequent cancer globally and the fourth most prevalent cancer among women. Automated and higher accuracy of cervical cancer classification methods are needed for the early diagnosis of cancer. In addition, this study has proved that routine Pap smears could enhance clinical outcomes by facilitating the early diagnosis of cervical cancer. Liquid-based cytology (LBC)/Pap smears for advanced cervical screening is a highly effective precancerous cell detection technology based on cell image analysis, where cells are classed as normal or abnormal. Computer-aided systems in medical imaging have benefited greatly from extraordinary developments in artificial intelligence (AI) technology. However, resource and computational cost constraints prevent the widespread use of AI-based automation-assisted cervical cancer screening systems. Hence, this paper reviewed the related studies that have been done by previous researchers related to the automation of cervical cancer classification based on machine learning. The objective of this study is to systematically review and analyses the current research on the classification of the cervical using machine learning. The literature that has been reviewed is indexed by Scopus and Web of Science. As a result, for the published paper access until October 2022, this study assessed past approaches for cervical cell classification based on machine learning applications.

Cervical cancer refers to a dangerous and common illness that impacts women worldwide. Moreover, this cancer affects over 300,000 people each year, with one woman diagnosed every minute. It affects over 0.5 million women annually, leading to over 0.3 million deaths. Recently, considerable literature has grown around developing technologies to detect cervical cancer cells in women. Previously, a cervical cancer diagnosis was made manually, which may result in a false positive or negative. Automated detection of cervical cancer and analysis method of the Papanicolaou (Pap) smear images are still debated among researchers. Thus, this paper reviewed several studies related to the detection method of Pap smear images focusing on Nuclei Segmentation and Deep Learning (DL) from the publication year of 2020, 2021, and 2022. Training, validation, and testing stages have all been the subject of study. However, there are still inadequacies in the current methodologies that have caused limitations to the proposed approaches by researchers. This study may inspire other researchers to view the proposed methods' potential and provide a decent foundation for developing and implementing new solutions.

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.