A Hybrid Deep Transfer Learning of CNN-Based LR-PCA for Breast Lesion Diagnosis via Medical Breast Mammograms

Nagwan Abdel Samee; Amel A Alhussan; Vidan Fathi Ghoneim; Ghada Atteia; Reem Alkanhel; Mugahed A Al-Antari; Yasser M Kadah

doi:10.3390/s22134938

A Hybrid Deep Transfer Learning of CNN-Based LR-PCA for Breast Lesion Diagnosis via Medical Breast Mammograms

Sensors (Basel). 2022 Jun 30;22(13):4938. doi: 10.3390/s22134938.

Authors

Nagwan Abdel Samee¹, Amel A Alhussan², Vidan Fathi Ghoneim³, Ghada Atteia¹, Reem Alkanhel¹, Mugahed A Al-Antari⁴, Yasser M Kadah^{5

6}

Affiliations

¹ Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
² Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
³ Biomedical Engineering Department, Helwan University, Cairo 11795, Egypt.
⁴ Department of Artificial Intelligence, College of Software & Convergence Technology, Daeyang AI Center, Sejong University, Seoul 05006, Korea.
⁵ Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah 22254, Saudi Arabia.
⁶ Biomedical Engineering Department, Cairo University, Giza 12613, Egypt.

Abstract

One of the most promising research areas in the healthcare industry and the scientific community is focusing on the AI-based applications for real medical challenges such as the building of computer-aided diagnosis (CAD) systems for breast cancer. Transfer learning is one of the recent emerging AI-based techniques that allow rapid learning progress and improve medical imaging diagnosis performance. Although deep learning classification for breast cancer has been widely covered, certain obstacles still remain to investigate the independency among the extracted high-level deep features. This work tackles two challenges that still exist when designing effective CAD systems for breast lesion classification from mammograms. The first challenge is to enrich the input information of the deep learning models by generating pseudo-colored images instead of only using the input original grayscale images. To achieve this goal two different image preprocessing techniques are parallel used: contrast-limited adaptive histogram equalization (CLAHE) and Pixel-wise intensity adjustment. The original image is preserved in the first channel, while the other two channels receive the processed images, respectively. The generated three-channel pseudo-colored images are fed directly into the input layer of the backbone CNNs to generate more powerful high-level deep features. The second challenge is to overcome the multicollinearity problem that occurs among the high correlated deep features generated from deep learning models. A new hybrid processing technique based on Logistic Regression (LR) as well as Principal Components Analysis (PCA) is presented and called LR-PCA. Such a process helps to select the significant principal components (PCs) to further use them for the classification purpose. The proposed CAD system has been examined using two different public benchmark datasets which are INbreast and mini-MAIS. The proposed CAD system could achieve the highest performance accuracies of 98.60% and 98.80% using INbreast and mini-MAIS datasets, respectively. Such a CAD system seems to be useful and reliable for breast cancer diagnosis.

Keywords: CAD system; breast cancer; breast lesion classification; deep feature extraction and reduction; hybrid CNN-based LR-PCA.

MeSH terms

Breast Neoplasms* / diagnostic imaging
Female
Humans
Logistic Models
Machine Learning
Mammography / methods
Neural Networks, Computer*

Grants and funding

(Grant No# FRP-1440-30)/Princess Nourah bint Abdulrahman University