This study aims to enable effective breast ultrasound image classification by combining deep features with conventional handcrafted features to classify the tumors. In particular, the deep features are extracted from a pre-trained convolutional neural network model, namely the VGG19 model, at six different extraction levels. The deep features extracted at each level are analyzed using a features selection algorithm to identify the deep feature combination that achieves the highest classification performance. Furthermore, the extracted deep features are combined with handcrafted texture and morphological features and processed using features selection to investigate the possibility of improving the classification performance. The cross-validation analysis, which is performed using 380 breast ultrasound images, shows that the best combination of deep features is obtained using a feature set, denoted by $CONV$ features that include convolution features extracted from all convolution blocks of the VGG19 model. In particular, the $CONV$ features achieved mean accuracy, sensitivity, and specificity values of $94.2\%$, $93.3\%$, and $94.9\%$, respectively. The analysis also shows that the performance of the $CONV$ features degrades substantially when the features selection algorithm is not applied. The classification performance of the $CONV$ features is improved by combining these features with handcrafted morphological features to achieve mean accuracy, sensitivity, and specificity values of $96.1\%$, $95.7\%$, and $96.3\%$, respectively. Furthermore, the cross-validation analysis demonstrates that the $CONV$ features and the combined $CONV$ and morphological features outperform the handcrafted texture and morphological features as well as the fine-tuned VGG19 model. The generalization performance of the $CONV$ features and the combined $CONV$ and morphological features is demonstrated by performing the training using the 380 breast ultrasound images and the testing using another dataset that includes 163 images. The results suggest that the combined $CONV$ and morphological features can achieve effective breast ultrasound image classifications that increase the capability of detecting malignant tumors and reduce the potential of misclassifying benign tumors.

中文翻译：

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结合深度和手工特征对超声图像中的干性肿瘤分类

这项研究旨在通过将深层特征与常规手工特征相结合来对肿瘤进行有效的乳房超声图像分类。特别是，深度特征是从预训练的卷积神经网络模型（即VGG19模型）中以六个不同的提取级别提取的。使用特征选择算法分析在每个级别提取的深度特征，以识别实现最高分类性能的深度特征组合。此外，将提取的深层特征与手工制作的纹理和形态特征相结合，并使用特征选择进行处理，以研究改善分类性能的可能性。使用380张乳房超声图像进行交叉验证分析，$CØñV$包括从VGG19模型的所有卷积块中提取的卷积特征的特征。特别是$CØñV$ 特征获得的平均准确度，敏感性和特异性值 $94.2％$， $93.3％$和 $94.9％$， 分别。分析还表明，$CØñV$不应用特征选择算法时，特征会大大降低。的分类性能$CØñV$ 通过将这些特征与手工形态特征相结合，可以改善特征的平均准确性，敏感性和特异性。 $96.1％$， $95.7％$和 $96.3％$， 分别。此外，交叉验证分析表明$CØñV$ 功能与综合 $CØñV$形态特征优于手工制作的纹理和形态特征以及经过微调的VGG19模型。的泛化性能$CØñV$ 功能与综合 $CØñV$通过使用380幅乳房超声图像进行训练并使用包含163幅图像的另一个数据集进行测试，可以证明其形态特征。结果表明$CØñV$ 形态特征可以实现有效的乳房超声图像分类，从而增加检测恶性肿瘤的能力并减少良性肿瘤分类错误的可能性。