Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection.

Journal: BioMed research international
Published Date:

Abstract

Breast cancer is the most common cancer in women, and the breast mass recognition model can effectively assist doctors in clinical diagnosis. However, the scarcity of medical image samples makes the recognition model prone to overfitting. A breast mass recognition model integrated with deep pathological information mining is proposed: constructing a sample selection strategy, screening high-quality samples across different mammography image datasets, and dealing with the scarcity of medical image samples from the perspective of data enhancement; mining the pathology contained in limited labeled models from shallow to deep information; and dealing with the shortage of medical image samples from the perspective of feature optimization. The multiview effective region gene optimization (MvERGS) algorithm is designed to refine the original image features, improve the feature discriminate and compress the feature dimension, better match the number of samples, and perform discriminate correlation analysis (DCA) on the advanced new features; in-depth cross-modal correlation between heterogeneous elements, that is, the deep pathological information, can be mined to describe the breast mass lesion area accurately. Based on deep pathological information and traditional classifiers, an efficient breast mass recognition model is trained to complete the classification of mammography images. Experiments show that the key technical indicators of the recognition model, including accuracy and AUC, are better than the mainstream baselines, and the overfitting problem caused by the scarcity of samples is alleviated.

Authors

  • Manisha Bhende
    Marathwada Mitra Mandal's Institute of Technology, Pune, India.
  • Anuradha Thakare
    Pimpri Chinchwad College of Engineering, Pune, India.
  • Bhasker Pant
    Department of Computer Science & Engineering, Graphic Era Deemed to Be University, Dehradun, Uttarakhand 248002, India.
  • Piyush Singhal
    Department of Mechanical Engineering, GLA University, Mathura 281406, India.
  • Swati Shinde
    Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune, India.
  • V Saravanan
    Dr. SNS Rajalakshmi College of Arts and Science, Coimbatore, India. tvsaran@hotmail.com.