Application of nanotechnology in breast cancer screening under obstetrics and gynecology through the use of CNN and ANFIS.

Autor: Zheng N; College of Pharmacy, Zhejiang Chinese Medical University, Hangzhou, 311402, China., Yao Z; Institute of Life Science, Wenzhou University, Wenzhou, 325035, China., Tao S; Institute of Life Science, Wenzhou University, Wenzhou, 325035, China., Almadhor A; Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka, 72388, Saudi Arabia., Alqahtani MS; Radiological Sciences Department, College of Applied Medical Sciences, King Khalid University, Abha, 61421, Saudi Arabia; BioImaging Unit, Space Research Centre, Michael Atiyah Building, University of Leicester, Leicester, LE1 7RH, UK., Ghoniem RM; Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia., Zhao H; College of Pharmacy, Zhejiang Chinese Medical University, Hangzhou, 311402, China. Electronic address: zhj@zcmu.edu.cn., Li S; Institute of Life Science, Wenzhou University, Wenzhou, 325035, China. Electronic address: 20170215@wzu.edu.cn.
Jazyk: angličtina
Zdroj: Environmental research [Environ Res] 2023 Oct 01; Vol. 234, pp. 116414. Date of Electronic Publication: 2023 Jun 28.
DOI: 10.1016/j.envres.2023.116414
Abstrakt: Breast cancer is the leading reason of death among women aged 35 to 54. Breast cancer diagnosis still presents significant challenges, and preventing the disease's most severe symptoms requires early detection. The role of nanotechnology in the tumor-treatment has recently attracted a lot of interest. In cancer therapies, nanotechnology plays a major role in the medication distribution process. Nanoparticles have the ability to target tumors. Nanoparticles are favorable and maybe preferable for usage in tumor detection and imaging due to their incredibly small size. Quantum dots, semiconductor crystals with increased labeling and imaging capabilities for cancer cells, are one of the particles that have received the most research attention. The design of the research is cross-sectional and descriptive. From April through September of 2020, data were gathered at the State Hospital. All pregnant women who came to the hospital throughout the first and second trimesters of the research's data collection were included in the study population. 100 pregnant women between the ages of 20 and 40 who had not yet had a mammogram comprised the research sample. 1100 digitized mammography images are included in the dataset, which was obtained from a hospital. Convolutional neural networks (CNN) were used to scan all images, and breast masses and mass comparisons were made using the malignant-benign categorization. The adaptive neuro-fuzzy inference system (ANFIS) then examined all of the data obtained by CNN in order to identify breast cancer early using inputs based on the nine different inputs. The precision of the mechanism used in this technique to determine the ideal radius value is significantly impacted by the radius value. Nine variables that define breast cancer indicators were utilized as inputs to the ANFIS classifier, which was then used to identify breast cancer. The parameters were given the necessary fuzzy functions, and the combined dataset was applied to train the method. Testing was initially performed by 30% of dataset that was later done with the real data obtained from the hospital. The accuracy of the results for 30% data was 84% (specificity =72.7%, sensitivity =86.7%) and the results for the real data was 89.8% (sensitivity =82.3%, specificity =75.9%), respectively.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023. Published by Elsevier Inc.)
Databáze: MEDLINE
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