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基于集成学习的乳腺图像混合分割用于基于模糊 C 均值和 CNN 模型的乳腺癌风险预测。

Ensemble Learning-Based Hybrid Segmentation of Mammographic Images for Breast Cancer Risk Prediction Using Fuzzy C-Means and CNN Model.

机构信息

Department of Computer Science and Engineering, School of Engineering, Kathmandu University, Banepa, Kathmandu, Nepal.

Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, P.O. Box. 151, Alkharj 11942, Saudi Arabia.

出版信息

J Healthc Eng. 2023 Jan 31;2023:1491955. doi: 10.1155/2023/1491955. eCollection 2023.

DOI:10.1155/2023/1491955
PMID:36760835
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9904922/
Abstract

The research interest in this field is that females are not aware of their health conditions until they develop tumour, especially when breast cancer is concerned. The breast cancer risk factors include genetics, heredity, and sedentary lifestyle. The prime concern for the mortality rate among females is breast cancer, and breast cancer is on the rise, both in rural and urban India. Women aged 45 or above are more vulnerable to this disease. Images are more effective at depicting information as compared to text. With the advancement in technology, several computerized techniques have come up to extract hidden information from the images. The processed images have found their application in several sectors and medical science is one of them. Disease-like breast cancer affects most women universally and it happens due to the existence of breast masses in the breast region for the development of breast cancer in women. Timely breast cancer detection can also increase the rate of effective treatment and the survival of women suffering from breast cancer. This work elaborates the method of performing hybrid segmentation techniques using CLAHE, morphological operations on mammogram images, and classified images using deep learning. Images from the MIAS database have been used to obtain readings for parameters: threshold, accuracy, sensitivity, specificity rate, biopsy rate, or a combination of all the parameters and many others under study.

摘要

该领域的研究兴趣在于，女性直到出现肿瘤才会意识到自己的健康状况，尤其是乳腺癌。乳腺癌的风险因素包括遗传、遗传易感性和久坐的生活方式。女性死亡率的主要关注点是乳腺癌，乳腺癌在印度农村和城市都呈上升趋势。45 岁以上的女性更容易受到这种疾病的影响。与文本相比，图像在描述信息方面更有效。随着技术的进步，已经出现了几种计算机化技术来从图像中提取隐藏信息。经过处理的图像已经在多个领域得到了应用，医学科学就是其中之一。像乳腺癌这样的疾病普遍影响着大多数女性，它是由于乳房区域存在乳房肿块而导致的，这些肿块会导致女性患上乳腺癌。及时发现乳腺癌也可以提高有效治疗的比率，并提高乳腺癌患者的生存率。这项工作详细阐述了使用 CLAHE 对乳房 X 光图像进行混合分割技术、形态学操作的方法，并使用深度学习对分类图像进行分类。使用 MIAS 数据库中的图像来获取参数的读数：阈值、准确性、敏感性、特异性率、活检率，或所有参数的组合以及许多其他正在研究的参数。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c90/9904922/2ddad322f85d/JHE2023-1491955.alg.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c90/9904922/4387d1259923/JHE2023-1491955.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c90/9904922/a6cfa8897e7d/JHE2023-1491955.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c90/9904922/3cbf5ef8de76/JHE2023-1491955.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c90/9904922/1288cdcb4794/JHE2023-1491955.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c90/9904922/54f5f954aa35/JHE2023-1491955.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c90/9904922/b61315d2071d/JHE2023-1491955.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c90/9904922/2666d5203186/JHE2023-1491955.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c90/9904922/fed7d0d4a695/JHE2023-1491955.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c90/9904922/2ddad322f85d/JHE2023-1491955.alg.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c90/9904922/4387d1259923/JHE2023-1491955.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c90/9904922/a6cfa8897e7d/JHE2023-1491955.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c90/9904922/3cbf5ef8de76/JHE2023-1491955.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c90/9904922/1288cdcb4794/JHE2023-1491955.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c90/9904922/54f5f954aa35/JHE2023-1491955.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c90/9904922/b61315d2071d/JHE2023-1491955.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c90/9904922/2666d5203186/JHE2023-1491955.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c90/9904922/fed7d0d4a695/JHE2023-1491955.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c90/9904922/2ddad322f85d/JHE2023-1491955.alg.001.jpg

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