ArticleSensors (Basel, Switzerland)2020
Breast Tumor Classification in Ultrasound Images Using Combined Deep and Handcrafted Features.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Explainable AI based hybrid DRM-Net transfer learning model for breast cancer detection and classification using ultrasound images.Scientific reports · 2025Article
- Variational mode directed deep learning framework for breast lesion classification using ultrasound imaging.Scientific reports · 2025Article
- Deep learning radiomics based on multimodal imaging for distinguishing benign and malignant breast tumours.Frontiers in medicine · 2024Article
- Graph neural network-based breast cancer diagnosis using ultrasound images with optimized graph construction integrating the medically significant features.Journal of cancer research and clinical oncology · 2023Article
- Ceusia-Breast: computer-aided diagnosis with contrast enhanced ultrasound image analysis for breast lesions.BMC medical imaging · 2023Article
- Benign and Malignant Breast Tumor Classification in Ultrasound and Mammography Images via Fusion of Deep Learning and Handcraft Features.Entropy (Basel, Switzerland) · 2023Article
- Deep learning based on ultrasound images assists breast lesion diagnosis in China: a multicenter diagnostic study.Insights into imaging · 2022Article
- Bio-Imaging-Based Machine Learning Algorithm for Breast Cancer Detection.Diagnostics (Basel, Switzerland) · 2022Article
- Computer Vision-Based Microcalcification Detection in Digital Mammograms Using Fully Connected Depthwise Separable Convolutional Neural Network.Sensors (Basel, Switzerland) · 2021Article
- Method for Diagnosis of Acute Lymphoblastic Leukemia Based on ViT-CNN Ensemble Model.Computational intelligence and neuroscience · 2021Article
- Malignancy pattern analysis of breast ultrasound images using clinical features and a graph convolutional network.Digital healthArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.