Abstract
Cancer is the foremost cause of death in the United States. Cancers of the breast and cervix affect more women than any other disease. Scanning the damaged areas can reveal whether or not a woman has external breast cancer. Patients who suspect they may have breast cancer can perform a self-examination. As a result, it's possible to catch it in the early stages in female patients. There are many ways to detect cervical cancer in women, but one of the most common is to use a vaginal ultrasound. Cervical cancer is the foremost cause of mortality in female patients because it is difficult to diagnose at an early stage and patients do not have any symptoms until they are in the terminal stages of the disease. Women's mortality rates can be reduced if they are diagnosed early. Because of the findings presented here, an approach to finding cervical cancer in its earliest stages is suggested in order to save the lives of women who are suffering from it. Another method proposed in this study is the Pruned Fuzzy Hypersphere Neural Network (PFHNN), which uses an Enhanced Lee Filter and colour space algorithm to transform the pixels in the original cervical picture into those in the multi-resolution space.
References
1. Topol E. Deep medicine: how artificial intelligence can make healthcare human again. Hachette UK; 2019 Mar 12.
2. Cohen PA, Jhingran A, Oaknin A, Denny L. Cervical cancer. The Lancet. 2019 Jan 12;393(10167):169-82.
3. Koh WJ, Greer BE, Abu-Rustum NR, Apte SM, Campos SM, Chan J, Cho KR, Cohn D, Crispens MA, DuPont N, Eifel PJ. Cervical cancer. Journal of the National Comprehensive Cancer Network. 2013 Mar 1;11(3):320-43.
4. Greer BE, Koh WJ, Abu-Rustum NR, Apte SM, Campos SM, Chan J, Cho KR, Copeland L, Crispens MA, DuPont N, Eifel PJ. Cervical cancer. Journal of the National Comprehensive Cancer Network. 2010 Dec 1;8(12):1388-416.
5. De Spiegeleire S, Maas M, Sweijs T. Artificial intelligence and the future of defense: strategic implications for small-and medium-sized force providers. The Hague Centre for Strategic Studies; 2017 May 17.
6. Achdou Y, Buera FJ, Lasry JM, Lions PL, Moll B. Partial differential equation models in macroeconomics. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2014 Nov 13;372(2028):20130397.
7. Pannekoucke O, Fablet R. PDE-NetGen 1.0: from symbolic partial differential equation (PDE) representations of physical processes to trainable neural network representations. Geoscientific
Model Development. 2020 Jul 30;13(7):3373-82.
8. Hurrah NN, Parah SA, Sheikh JA, Al-Turjman F, Muhammad K. Secure data transmission framework for confidentiality in IoTs. Ad Hoc Networks. 2019 Dec 1;95:101989.
9. Wang Y, Sun Z, Xu D, Wu L, Chang J, Tang L, Jiang Z, Jiang B, Wang G, Chen T, Feng H. A hybrid method based region of interest segmentation for continuous wave terahertz imaging. Journal of Physics D: Applied Physics. 2019 Dec 18;53(9):095403.
10. Chen D, Wan S, Xiang J, Bao FS. A high-performance seizure detection algorithm based on Discrete Wavelet Transform (DWT) and EEG. PloS one. 2017 Mar 9;12(3):e0173138.
11. Gupta, S, Nijhawan, R, Suri, V, Kaur, A, Bhasin, V & Arora, SK 2006, ‘Prevalence of high-risk human papillomavirus infection in women with benign cervical cytology: a hospital based study from North India’, Indian Journal of Cancer, vol. 43, no. 3, pp. 110-116
12. Bergmeir, C, Silvente, MG & Benitez, JM 2012, ‘Segmentation of cervical cell nuclei in high-resolution microscopic images: a new algorithm and a web-based software framework’, Computer Methods and Programs in Biomedicine, vol. 107, no. 3, pp. 497-512
13. Fadahunsi, OO, Omoniyi-Esan, GO, Banjo, AAF, Esimai, OA, Osiagwu, D, Clement, F, Adeteye, OV, Bejide, RA & Iyiola, S 2013, ‘Prevalence of high risk oncogenic Human Papillomavirus types in cervical smears of women attending Well woman clinic in Ile Ife, Nigeria’, Gynecology & Obstetrics, vol. 3, no. 6, pp. 1-5
14. Doorbar, J, Quint, W, Banks, L, Bravo, IG, Stoler, M, Broker, TR & Stanley, MA 2012, ‘The biology and life-cycle of human papillomaviruses’, Vaccine, vol. 30 (Suppl 5), pp. F55-F70.
15. ahn, JA, Lan, D & Kahn, RS 2007, ‘Sociodemographic factors associated with high risk human papillomavirus
infection’, Obstet gynecol., vol. 110, no. 1, pp. 87-95
16. Alyafeai Z, Ghouti L. A fully-automated deep learning pipeline for cervical cancer classification. Expert Systems with Applications. 2020 Mar 1;141:112951.
17. Jaya BK, Kumar SS. Image registration based cervical cancer detection and segmentation using ANFIS classifier. Asian Pacific Journal of Cancer Prevention: APJCP. 2018;19(11):3203.