Histopathological analyses of breast cancer using deep learning

Cardiometry

article

C. Ravindra Murthy¹  K. Balaji² 

[1] Research Scholar, Department of Electrical Electronics Engineering, St.Peter’s Institute of Higher Education and Research;Department of Electrical Electronics Engineering, St.Peter’s Institute of Higher Education and Research

关键词: Histopathological images;    Breast cancer;    Deep learning;    Floating image;    Convolution Neural Network;    Automated diagnosis;   

DOI  :  10.18137/cardiometry.2022.22.456461

学科分类：环境科学（综合）

来源: Russian New University

【 摘 要 】

Deep Learning hosts a plethora of variants and models in Convolution Neural Networks (CNN), where the prudence of these methods is algorithmically proven when implemented with sturdy datasets. Much number of haphazard structures and textures are found in the histopathological images of breast cancer, where dealing with such multicolor and multi-structure components in the images is a challenging task. Working with such data in wet labs proves clinically consistent results, but added with the computational models will improvise them empirically. In this paper, we proposed a model to diagnose breast cancer using raw images of breast cancer with different resolutions, irrespective of the structures and textures. The floating image is mapped with the healthy reference image and examined using different statistics such as cross correlations and phase correlations. Experiments are carried out with the aim of establishing the optimal performance on histopathological images. The model attained satisfactory results and are proved good for decision making in cancer diagnosis.

【 授权许可】

CC BY