Open AccessA quantization assisted U-Net study with ICA and deep features fusion for breast cancer identification using ultrasonic data
Author(s) -
Talha Meraj,
Wael Alosaimi,
Bader Alouffi,
Hafiz Tayyab Rauf,
Swarn Avinash Kumar,
Robertas Damaševičius,
Hashem Alyami
Publication year - 2021
Publication title -
peerj. computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.806
H-Index - 24
ISSN - 2376-5992
DOI - 10.7717/peerj-cs.805
Subject(s) - artificial intelligence , computer science , breast cancer , segmentation , breast ultrasound , pattern recognition (psychology) , independent component analysis , ultrasonic sensor , quantization (signal processing) , robustness (evolution) , computer vision , mammography , radiology , medicine , cancer , biology , biochemistry , gene
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.