A Review on Computational Methods for Breast Cancer Detection in Ultrasound Images Using Multi-Image Modalities

被引:0
作者
Sushi Sushanki
Ashish Kumar Bhandari
Amit Kumar Singh
机构
[1] National Institute of Technology Patna,Department of Electronics and Communication Engineering
来源
Archives of Computational Methods in Engineering | 2024年 / 31卷
关键词
D O I
暂无
中图分类号
学科分类号
摘要
Breast cancer is a kind of cancer that develops and propagates from tissues of the breast and slowly transcends the whole body, this type of tumor is found in both sexes. Early detection of this disease is very important as at this stage it can be controlled by giving patients the required treatment and their valuable life can be saved. Researchers and scientists according to various studies have found methods to detect cancer at the initial stages, however, misperception in identifying skeptical lesions can be due to poor image quality and diverse breast density. Breast cancer (BC) is still a major concern for world health, necessitating ongoing innovation in early diagnosis and detection. Breast cancer diagnosis has made significant strides in recent years, especially with the incorporation of multi-modal imaging modalities. This article provides a summary of the most recent methods and advancements in multi-modal imaging for the detection of breast cancer. When radiomics, a quantitative study of imaging data, is integrated with machine learning and deep learning algorithms, breast lesions have demonstrated potential. These techniques can help distinguish between benign and malignant tumours, providing physicians with crucial information.At various phases of breast cancer detection, new methods have been developed for enhancement, segmentation, feature extraction, and classification employing multiple picture modalities. This review paper‘s objective is to represent all prior research in the area of breast cancer categorization utilising many imaging modalities. This paper provides a thorough and rigorous examination of current trends in the field of BC detection and classification.
引用
收藏
页码:1277 / 1296
页数:19
相关论文
共 328 条
  • [1] Saber A(2021)A novel deep-learning model for automatic detection and classification of breast cancer using the transfer-learning technique IEEE Access 9 71194-71209
  • [2] Sakr M(2019)A novel deep learning based framework for the detection and classification of breast cancer using transfer learning” Pattern Recognit Lett. 125 1-6
  • [3] Abo-Seida OM(2019)Accurate prediction of neoadjuvant chemotherapy pathological complete remission (PCR) for the four sub-types of breast cancer IEEE Access 7 134697-134706
  • [4] Keshk A(2018)Computer-aided theragnosis based on tumor volumetric information in breast cancer IEEE Trans Ultrason Ferroelectr Freq Control 65 1359-1369
  • [5] Chen H(2016)Computer aided theragnosis using quantitative ultrasound spectroscopy and maximum mean discrepancy in locally advanced breast cancer IEEE Trans Med Imaging 35 778-790
  • [6] Khan SU(2016)Contextual atlas regression forests: multiple-atlas-based automated dose prediction in radiation therapy IEEE Trans Med Imaging 35 1000-1012
  • [7] Islam N(2020)Prediction of breast cancer, comparative review of machine learning techniques, and their analysis IEEE Access 8 150360-150376
  • [8] Jan Z(2013)Progression from ductal carcinoma in situ to invasive breast cancer: revisited Mol Oncol 7 859-869
  • [9] Ud Din I(2020)Automated invasive ductal carcinoma detection based using deep transfer learning with whole-slide images Pattern Recognit Lett 133 232-239
  • [10] Rodrigues JJPC(1982)Histogenesis of salivary gland pleomorphic adenoma (mixed tumor) with an evaluation of the role of the myoepithelial cell Hum Pathol 13 62-75