In Current medical imaging
BACKGROUND : Breast cancer (BC) is one of the most severe diseases in women. Therefore, a premature diagnosis is necessary for timely detection and treatment execution. Clinical-level diagnosis of BC is normally performed with imaging techniques, and Ultrasound-Imaging (UI) is one of the non-invasive imaging techniques frequently executed to diagnose BC.
AIMS : This research aims to develop an efficient deep-learning framework to detect BC from UI with better accuracy.
METHODS : The executed method consists of the following stages: (i) Data collection and pre-processing, (ii) Deep-features mining with pre-trained VGG16, (iii) Image enhancement using Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP), (iv) Firefly-algorithm (FA) supported feature reduction, and (v) Feature integration and classification.
RESULTS : The proposed work is tested and executed using 1680 test images (840 benign and 840 malignant) of dimension pixels and implements a binary classifier with 5-fold cross-validation to separate the UI database into the healthy/cancer class.
CONCLUSION : This work implemented FA-supported feature reduction. Moreover, it was found that this scheme helps to achieve a classification accuracy of 98.21% with the KNN classifier.
Yang Yanyan, Liu Qiaojian, Dai Ting, Zhang Haijun
2023-Jan-20
Breast cancer, Deep-Learning, Feature optimization, Ultrasound image, VGG16, classification.
