Open Access

ARTICLE

Breast Mammogram Analysis and Classification Using Deep Convolution Neural Network

V. Ulagamuthalvi¹, G. Kulanthaivel^2,*, A. Balasundaram³, Arun Kumar Sivaraman⁴

1 Department of Computer Science and Engineering, Sathyabama University, Chennai, 600119, India
2 Department of Electrical, Electronics and Communication Engineering, NITTTR, Chennai, 600113, India
3 Centre for Cyber Physical Systems, School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Chennai, 600127, India
4 School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Chennai, 600127, India

* Corresponding Author: G. Kulanthaivel. Email:

Computer Systems Science and Engineering 2022, 43(1), 275-289. https://doi.org/10.32604/csse.2022.023737

Received 19 September 2021; Accepted 27 October 2021; Issue published 23 March 2022

Abstract

One of the fast-growing disease affecting women’s health seriously is breast cancer. It is highly essential to identify and detect breast cancer in the earlier stage. This paper used a novel advanced methodology than machine learning algorithms such as Deep learning algorithms to classify breast cancer accurately. Deep learning algorithms are fully automatic in learning, extracting, and classifying the features and are highly suitable for any image, from natural to medical images. Existing methods focused on using various conventional and machine learning methods for processing natural and medical images. It is inadequate for the image where the coarse structure matters most. Most of the input images are downscaled, where it is impossible to fetch all the hidden details to reach accuracy in classification. Whereas deep learning algorithms are high efficiency, fully automatic, have more learning capability using more hidden layers, fetch as much as possible hidden information from the input images, and provide an accurate prediction. Hence this paper uses AlexNet from a deep convolution neural network for classifying breast cancer in mammogram images. The performance of the proposed convolution network structure is evaluated by comparing it with the existing algorithms.

---

Keywords

---