ArticleScientific reports2021
Classification of malignant tumours in breast ultrasound using unsupervised machine learning approaches.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- N-Unet: An Efficient Multi-Task Model for Precise Classification and Segmentation of Breast Ultrasound Images.Journal of imaging · 2026Article
- Application of YOLO-v7 and YOLO-v8 Transfer Learning Models in Breast Lesion Classification and DiagnosisCurrent medical imaging · 2026Article
- 3DSN-net: dual-tandem attention mechanism interaction network for breast tumor classification.BMC medical imaging · 2025Article
- Breast Cancer Detection via Multi-Tiered Self-Contrastive Learning in Microwave Radiometric Imaging.Diagnostics (Basel, Switzerland) · 2025Article
- A Systematic Review of Species Classification Using Deep Learning Algorithms and Gender Identification ofZoological studies · 2025Article
- Article
- Forward layer-wise learning of convolutional neural networks through separation index maximizing.Scientific reports · 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
- Improving Breast Cancer Detection and Diagnosis through Semantic Segmentation Using the Unet3+ Deep Learning Framework.Biomedicines · 2023Article
- Ultrasound-based deep learning in the establishment of a breast lesion risk stratification system: a multicenter study.European radiology · 2023Article
- Artificial Intelligence in Breast Ultrasound: From Diagnosis to Prognosis-A Rapid Review.Diagnostics (Basel, Switzerland) · 2022Review
- Semantic Segmentation of the Malignant Breast Imaging Reporting and Data System Lexicon on Breast Ultrasound Images by Using DeepLab v3.Sensors (Basel, Switzerland) · 2022Article
- Incorporating the Breast Imaging Reporting and Data System Lexicon with a Fully Convolutional Network for Malignancy Detection on Breast Ultrasound.Diagnostics (Basel, Switzerland) · 2021Article
- Dilated Semantic Segmentation for Breast Ultrasonic Lesion Detection Using Parallel Feature Fusion.Diagnostics (Basel, Switzerland) · 2021Article
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Authors and funding
3 authors.
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Abstract
Traditional computer-aided diagnosis (CAD) processes include feature extraction, selection, and classification. Effective feature extraction in CAD is important in improving the classification's performance. We introduce a machine-learning method and have designed an analysis procedure of benign and malignant breast tumour classification in ultrasound (US) images without a need for a priori tumour region-selection processing, thereby decreasing clinical diagnosis efforts while maintaining high classification performance. Our dataset constituted 677 US images (benign: 312, malignant: 365). Regarding two-dimensional US images, the oriented gradient descriptors' histogram pyramid was extracted and utilised to obtain feature vectors. The correlation-based feature selection method was used to evaluate and select significant feature sets for further classification. Sequential minimal optimisation-combining local weight learning-was utilised for classification and performance enhancement. The image dataset's classification performance showed an 81.64% sensitivity and 87.76% specificity for malignant images (area under the curve = 0.847). The positive and negative predictive values were 84.1 and 85.8%, respectively. Here, a new workflow, utilising machine learning to recognise malignant US images was proposed. Comparison of physician diagnoses and the automatic classifications made using machine learning yielded similar outcomes. This indicates the potential applicability of machine learning in clinical diagnoses.
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