Affiliations: [a]
Department of Computer Science and Engineering, St. Joseph’s Institute of Technology, Chennai, Tamilnadu, India
| [b]
Department of Electronics and Instrumentation Engineering, St. Joseph’s College of Engineering, Chennai, Tamilnadu, India
| [c]
Department of Information Technology, St. Joseph’s Institute of Technology, Chennai, Tamilnadu, India
Correspondence:
[*]
Corresponding author: K. Vijayakumar, Department of Computer Science and Engineering, St. Joseph’s Institute of Technology, Chennai 600 119, Tamilnadu, India. E-mail: mkvijay@msn.com.
Abstract: BACKGROUND:The incidence rates of breast cancer in women community is progressively raising and the premature diagnosis is necessary to detect and cure the disease. OBJECTIVE:To develop a novel automated disuse detection framework to examine the Breast-Ultrasound-Images (BUI). METHODS:This scheme includes the following stages; (i) Image acquisition and resizing, (ii) Gaussian filter-based pre-processing, (iii) Handcrafted features extraction, (iv) Optimal feature selection with Mayfly Algorithm (MA), (v) Binary classification and validation. The dataset includes BUI extracted from 133 normal, 445 benign and 210 malignant cases. Each BUI is resized to 256×256×1 pixels and the resized BUIs are used to develop and test the new scheme. Handcrafted feature-based cancer detection is employed and the parameters, such as Entropies, Local-Binary-Pattern (LBP) and Hu moments are considered. To avoid the over-fitting problem, a feature reduction procedure is also implemented with MA and the reduced feature sub-set is used to train and validate the classifiers developed in this research. RESULTS:The experiments were performed to classify BUIs between (i) normal and benign, (ii) normal and malignant, and (iii) benign and malignant cases. The results show that classification accuracy of > 94%, precision of > 92%, sensitivity of > 92% and specificity of > 90% are achieved applying the developed new schemes or framework. CONCLUSION:In this work, a machine-learning scheme is employed to detect/classify the disease using BUI and achieves promising results. In future, we will test the feasibility of implementing deep-learning method to this framework to further improve detection accuracy.
