Breast ultrasound tumor image classification using image decomposition and fusion based on adaptive multi-model spatial feature fusion. (September 2021) - Zhuang, Zhemin; Yang, Zengbiao; Raj, Alex Noel Joseph; Wei, Chuliang; Jin, Pengcheng; Zhuang, Shuxin
Highlights: A variety of image decomposition methods are proposed based on the original breast ultrasound image and the lesion area's mask image, which effectively augment the types of decompose images. Different image fusion methods are proposed to fuse the decompose images to highlight the clinical features and reduce the interference of irrelevant noise in the original image. Based on the transfer learning method, the best base models for extracting the deep learning features of fusion images are selected by comparing various deep learning models. Through the adaptive spatial feature fusion technology, a variety of deep learning features are fused. The comparative experiments prove that the proposed fusion method is better than the previous methods. Abstract: Background and Objective: Breast cancer is a fatal threat to the health of women. Ultrasonography is a common method for the detection of breast cancer. Computer-aided diagnosis of breast ultrasound images can help doctors in diagnosing benign and malignant lesions. In this paper, by combining image decomposition and fusion techniques with adaptive spatial feature fusion technology, a reliable classification method for breast ultrasound images of tumors is proposed. Methods: First, fuzzy enhancement and bilateral filtering algorithms are used to process the original breast ultrasound image. Then, various decomposition images representing the clinical characteristics of breast tumors are obtained using the original and mask images. By considering the diversity of the benign and malignant characteristic information represented by each decomposition image, the decomposition images are fused through the RGB channel, and three types of fusion images are generated. Then, from a series of candidate deep learning models, transfer learning is used to select the best model as the base model to extract deep learning features. Finally, while training the classification network, adaptive spatial feature fusion technology is used to train the weight network to complete deep learning feature fusion and classification. Results: In this study, 1328 breast ultrasound images were collected for training and testing. The experimental results show that the values of accuracy, precision, specificity, sensitivity/recall, F1 score, and area under the curve of the proposed method were 0.9548, 0.9811, 0.9833, 0.9392, 0.9571, and 0.9883, respectively. Conclusion: Our research can automate breast cancer detection and has strong clinical utility. When compared to previous methods, our proposed method is expected to be more effective while assisting doctors in diagnosing breast ultrasound images.