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.
Highlights
Traditional computer-aided diagnosis (CAD) processes include feature extraction, selection, and classification
The aims of this study were to (a) increase the diagnostic performance associated with the classification of malignant tumours belonging to Breast Imaging Reporting and Data System (BI-RADS) category 4 in US images, and (b) achieve comparable performance to those reported for deep learning techniques that are based on the cooperation of several machine learning algorithms
After applying the pyramid histogram of oriented gradients (PHOG) descriptor calculation to extract the feature vectors, 630 attributes were extracted from each US image of the dataset, and 60 attributes were preserved after applying the feature selection
Summary
Traditional computer-aided diagnosis (CAD) processes include feature extraction, selection, and classification. For the improvement of risk assessment and quality of care, the Breast Imaging Reporting and Data System (BI-RADS)[2] provides standardised terms for describing breast mass features and assessments in radiology, including mammography, magnetic resonance imaging (MRI), and US. This approach has been proven to be effective to distinguish between benign and malignant masses[3]. To improve diagnostic accuracy and reduce differences among observers, CAD systems have been used to distinguish between malignant and benign masses in ultrasound images of breast c ancers[6,7].
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