CNN-Wavelet scattering textural feature fusion for classifying breast tissue in mammograms

• Published in: Biomedical Signal Processing and Control
• Main Author: Razali N.F.; Isa I.S.; Sulaiman S.N.; A. Karim N.K.; Osman M.K.
• Format: Article
• Language: English
• Published: Elsevier Ltd 2023
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148545076&doi=10.1016%2fj.bspc.2023.104683&partnerID=40&md5=fc099798a85e90c04862ba9b2bf1b32c

Visual interpretation from radiologists employs computer-aided diagnosis (CAD) to make clinical diagnoses by analyzing breast tissue images and assessing their texture. Aside from needing more training images for Convolutional Neural Network (CNN), selecting textural feature input to the deep learning-based CAD is challenging to control since the entire feature map is extracted automatically and only considers spatial domain analysis. Hence, integrating spatial and frequency information from the images requires complex feature representation in a classification problem. Wavelet representation could improve the feature descriptors in acquiring information from the spatial-frequency analysis. However, wavelet transforms’ shift insensitivity may affect class feature representation by suppressing high-frequency information. This study uses CNN, and wavelet scattering (WS) features to classify fatty and fibroglandular tissue with benign and malignant masses from digital mammograms to overcome CNN overfitting based on a limited dataset. Scattered WS coefficients formed from layers of wavelet dilations and averaging are utilized to retain the loss of high-frequency signal from the images, while simultaneously, the added CNN features improve sparse image representation resulting from convoluted spatial information of the images. Finally, the model is cascaded with an ensemble classifier for classifying fatty and fibroglandular tissue and mass images, n = 112, from the INbreast mammogram dataset. The best performance of mass and breast tissue classification models reach 98.0 % and 99.3 % on 10-fold cross-validation accuracy. By combining textural features from WS and deep learning model architecture, the model may adopt a training system with fewer data without augmentation to achieve good performance and low complexity costs. © 2023 Elsevier Ltd