1. Detection and diagnosis of cervical cancer using U-net algorithm.
- Author
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Mukil, A., Mohan, S. B., Alexander, C. H. C., and Yvette, S. L. S.
- Subjects
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CERVICAL cancer diagnosis , *PAP test , *MACHINE learning , *ARTIFICIAL intelligence , *MEDICAL screening - Abstract
In many parts of the world, doctors often provide cervical cancer diagnoses to female patients. This malignancy is fourth more prevalent kind of carcinoma in ladies as well as the seventh most prevalent form of the illness overall. In order to diagnose cancer in its earliest stages, we need categorization approaches for cervical cancer that are both more accurate and more easily automated. Also, the results of this research demonstrated that having regular Pap screens performed might improve clinical outcomes by assisting in the earlier identification of cervical cancer. (LBC) Liquid-based cytology, sometimes referred to as Pap smears, is a method for detecting precancerous cells which has proven to become very effective for advanced cervical screening. This method identifies whether cells are normal or pathological based on cell picture analysis. (AI)Artificial intelligence technology has made amazing strides recently, and as a result, computer-aided systems in the area of healthcare scanning have reaped the benefits of these advances to a significant degree. Nevertheless, the limited availability of resources and the high computing costs involved find it challenging for AI-based automation-assisted cervix carcinoma monitoring systems to find widespread use. In view of the aforementioned, the goal of such an investigation would be to perform a review of relevant literature mostly on pertinent findings which previous researchers had carried out in connection to the automating of categorization of cervix cancer using machine learning techniques. The purpose of our research is to do a comprehensive analysis of the existing body of literature and then an in-depth analysis of the most recent and cutting-edge studies upon this categorization of certain cervix conditions by making use of machine learning. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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