An Interpretable Breast Ultrasound Image Classification Algorithm Based on Convolutional Neural Network and Transformer

Xiangjia Meng (), Jun Ma, Feng Liu, Zhihua Chen and Tingting Zhang
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Xiangjia Meng: School of Information Engineering, Shandong Youth University of Political Science, Jinan 250103, China
Jun Ma: School of Cyber Science and Engineering, Southeast University, Nanjing 219302, China
Feng Liu: School of Information Engineering, Shandong Youth University of Political Science, Jinan 250103, China
Zhihua Chen: School of Information Engineering, Shandong Youth University of Political Science, Jinan 250103, China
Tingting Zhang: School of Information Engineering, Shandong Youth University of Political Science, Jinan 250103, China

Mathematics, 2024, vol. 12, issue 15, 1-14

Abstract: Breast cancer is one of the most common causes of death in women. Early signs of breast cancer can be an abnormality depicted on breast images like breast ultrasonography. Unfortunately, ultrasound images contain a lot of noise, which greatly increases the difficulty for doctors to interpret them. In recent years, computer-aided diagnosis (CAD) has been widely used in medical images, reducing the workload of doctors and the probability of misdiagnosis. However, it still faces the following challenges in clinical practice: one is the lack of interpretability, and another is that the accuracy is not high enough. In this paper, we propose a classification model of breast ultrasound images that leverages tumor boundaries as prior knowledge and strengthens the model to guide classification. Furthermore, we employ the advantages of convolutional neural network (CNN) to extract local features and Transformer to extract global features to achieve information balance and complementarity between the two neural network models which increase the recognition performance of the model. Additionally, an explanation method is used to generate visual results, thereby improving the poor interpretability of deep learning models. Finally, we evaluate the model on the BUSI dataset and compare it with other CNN and Transformer models. Experimental results show that the proposed model obtains an accuracy of 0.9870 and an F1 score of 0.9872, achieving state-of-the-art performance.

Keywords: breast cancer; ultrasound imaging classification; artificial intelligence; ensemble learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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