BU-DLNet: Breast Ultrasonography-Based Cancer Detection Using Deep-Learning Network Selection and Feature Optimization

Abstract

Early detection of breast lesions and distinguishing between malignant and benign lesions are critical for breast cancer (BC) prognosis. Breast ultrasonography (BU) is an important radiological imaging modality for the diagnosis of BC. This study proposes a BU image-based framework for the diagnosis of BC in women. Various pre-trained networks are used to extract the deep features of the BU images. Ten wrapper-based optimization algorithms, including the marine predator algorithm, generalized normal distribution optimization, slime mold algorithm, equilibrium optimizer (EO), manta-ray foraging optimization, atom search optimization, Harris hawks optimization, Henry gas solubility optimization, path finder algorithm, and poor and rich optimization, were employed to compute the optimal subset of deep features using a support vector machine classifier. Furthermore, a network selection algorithm was employed to determine the best pre-trained network. An online BU dataset was used to test the proposed framework. After comprehensive testing and analysis, it was found that the EO algorithm produced the highest classification rate for each pre-trained model. It produced the highest classification accuracy of 96.79%, and it was trained using only a deep feature vector with a size of 562 in the ResNet-50 model. Similarly, the Inception-ResNet-v2 had the second highest classification accuracy of 96.15% using the EO algorithm. Moreover, the results of the proposed framework are compared with those in the literature.

Keywords: breast cancer (BC); breast ultrasonography (BU); image processing; optimization; wrapper-based method.

Figure 1

Extraction of deep features using a pre-trained deep-learning model.

Figure 2

Workflow for the wrapper-based approach.

Figure 3

(a) Proposed flowchart for the diagnosis of BC; (b) Network selection approach.

Figure 4

BU image classification that used the full deep features of various pre-trained models trained with SVM; whiskers represent the range.

Figure 5

Classification accuracy of various wrapper-based optimization approaches for BU images: (a) MPA; (b) GNDO; (c) SMA; (d) EO; (e)MRFO; (f) ASO; (g) HHO; (h) HGSO; (i) PFA; (j) PRO, where whiskers represent the range.

Figure 5

Classification accuracy of various wrapper-based optimization approaches for BU images: (a) MPA; (b) GNDO; (c) SMA; (d) EO; (e)MRFO; (f) ASO; (g) HHO; (h) HGSO; (i) PFA; (j) PRO, where whiskers represent the range.

Figure 6

Average processing time of each wrapper-based method.