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Ultrasound (US) is a common and economical method for the diagnosis of breast cancer. Breast imaging reporting and data system (BI\u2010RADS) category 4 has the highest false\u2010positive value of about 30% among five categories. The classification task in BI\u2010RADS category 4 is challenging and has not been fully studied.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>This work aimed to use convolutional neural networks (CNNs) for breast tumor classification using B\u2010mode images in category 4 to overcome the dependence on operator and artifacts. Additionally, this work intends to take full advantage of morphological and textural features in breast tumor US images to improve classification accuracy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>First, original US images coming directly from the hospital were cropped and resized. In 1385 B\u2010mode US BI\u2010RADS category 4 images, the biopsy eliminated 503 samples of benign tumor and left 882 of malignant. Then, K\u2010means clustering algorithm and entropy of sliding windows of US images were conducted. Considering the diversity of different characteristic information of malignant and benign represented by original B\u2010mode images, K\u2010means clustering images and entropy images, they are fused in a three\u2010channel form multi\u2010feature fusion images dataset. The training, validation, and test sets are 969, 277, and 139. With transfer learning, 11 CNN models including DenseNet and ResNet were investigated. Finally, by comparing accuracy, precision, recall, F1\u2010score, and area under curve (AUC) of the results, models which had better performance were selected. The normality of data was assessed by Shapiro\u2010Wilk test. DeLong test and independent <jats:italic>t<\/jats:italic>\u2010test were used to evaluate the significant difference of AUC and other values. False discovery rate was utilized to ultimately evaluate the advantages of CNN with highest evaluation metrics. In addition, the study of anti\u2010log compression was conducted but no improvement has shown in CNNs classification results.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>With multi\u2010feature fusion images, DenseNet121 has highest accuracy of 80.22\u00a0\u00b1\u00a01.45% compared to other CNNs, precision of 77.97\u00a0\u00b1\u00a02.89% and AUC of 0.82\u00a0\u00b1\u00a00.01. Multi\u2010feature fusion improved accuracy of DenseNet121 by 1.87% from classification of original B\u2010mode images (<jats:italic>p<\/jats:italic>\u00a0&lt;\u00a00.05).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The CNNs with multi\u2010feature fusion show a good potential of reducing the false\u2010positive rate within category 4. The work illustrated that CNNs and fusion images have the potential to reduce false\u2010positive rate in breast tumor within US BI\u2010RADS category 4, and make the diagnosis of category 4 breast tumors to be more accurate and precise.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16946","type":"journal-article","created":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T07:25:29Z","timestamp":1709537129000},"page":"4243-4257","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Classification of multi\u2010feature fusion ultrasound images of breast tumor within category 4 using convolutional neural networks"],"prefix":"10.1002","volume":"51","author":[{"given":"Pengfei","family":"Xu","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering Key Laboratory of Biomedical Information Engineering of Ministry of Education School of Life Science and Technology Xi'an Jiaotong University  Xi'an 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