A quantization assisted U-Net study with ICA and deep features fusion for breast cancer identification using ultrasonic data

Talha Meraj; Wael Alosaimi; Bader Alouffi; Hafiz Tayyab Rauf; Swarn Avinash Kumar; Robertas Damaševičius; Hashem Alyami

doi:10.7717/peerj-cs.805

A quantization assisted U-Net study with ICA and deep features fusion for breast cancer identification using ultrasonic data

PeerJ Comput Sci. 2021 Dec 16:7:e805. doi: 10.7717/peerj-cs.805. eCollection 2021.

Authors

Talha Meraj¹, Wael Alosaimi², Bader Alouffi³, Hafiz Tayyab Rauf⁴, Swarn Avinash Kumar⁵, Robertas Damaševičius⁶, Hashem Alyami³

Affiliations

¹ Department of Computer Science, COMSATS University Islamabad-Wah Campus, Wah Cantt, Pakistan.
² Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia.
³ Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia.
⁴ Department of Computer Science, Faculty of Engineering & Informatics, University of Bradford, Bradford, United Kingdom.
⁵ Department of Information Technology, Indian Institute of Information Technology, Uttar Pradesh, Jhalwa, Prayagraj, India.
⁶ Faculty of Applied Mathematics, Silesian University of Technology, Gliwice, Poland.

Abstract

Breast cancer is one of the leading causes of death in women worldwide-the rapid increase in breast cancer has brought about more accessible diagnosis resources. The ultrasonic breast cancer modality for diagnosis is relatively cost-effective and valuable. Lesion isolation in ultrasonic images is a challenging task due to its robustness and intensity similarity. Accurate detection of breast lesions using ultrasonic breast cancer images can reduce death rates. In this research, a quantization-assisted U-Net approach for segmentation of breast lesions is proposed. It contains two step for segmentation: (1) U-Net and (2) quantization. The quantization assists to U-Net-based segmentation in order to isolate exact lesion areas from sonography images. The Independent Component Analysis (ICA) method then uses the isolated lesions to extract features and are then fused with deep automatic features. Public ultrasonic-modality-based datasets such as the Breast Ultrasound Images Dataset (BUSI) and the Open Access Database of Raw Ultrasonic Signals (OASBUD) are used for evaluation comparison. The OASBUD data extracted the same features. However, classification was done after feature regularization using the lasso method. The obtained results allow us to propose a computer-aided design (CAD) system for breast cancer identification using ultrasonic modalities.

Keywords: Breast cancer; Computer vision; Features fusion; Image processing; Quantization; Ultrasonic images.

Grants and funding

This research was supported by Taif University Researchers Supporting Project number (TURSP-2020/306), Taif University, Taif, Saudi Arabia. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.