Automatic Grading System for Diagnosis of Breast Cancer Exploiting Co-occurrence Shearlet Transform and Histogram Features

• Published in: Ingénierie et recherche biomédicale 2020-04, Vol.41 (2), p.106-114
• Main Authors: Budak, Ü., Güzel, A.B.
• Format: Article
• Language: English
• Subjects: Breast cancer diagnosis; Co-occurrence of Shearlet transform; Color features; Histopathological image
• ISSN: 1959-0318
• DOI: 10.1016/j.irbm.2020.02.001

•This study aims to effectively determine the type of BC from histopathological images.•Both color and texture based hybrid method is used to grade the BC type.•The efficacy of the method is confirmed by statistical analyses.•The proposed method outperforms the existing methods. Breast cancer (BC) is one of the most commonly reported health issues worldwide, especially in females. Early detection and diagnosis of BC can greatly reduce mortality rates. Samples obtained with different imaging methods such as mammography, computerized tomography, magnetic resonance, ultrasound, and biopsy are used in the diagnosis of BC. Histopathological images obtained from a biopsy contain vital information about the stage of the BC. Computer-aided systems are important tools to assist pathologists in the early detection of BC. In the current study, the use of gray-level co-occurrence matrix (GLCM) of Shearlet Transform (ST) coefficients were first scrutinized as textural features. ST is an advanced decomposition-based method that can analyze images in various directions and is sensitive to edge singularities. These features make ST more robust than other decomposition methods such as Fourier and wavelet. Color channel histogram features were also utilized for a second level of evaluation in the diagnosis of the BC stage. These features are considered one of the most important building blocks that pathologists consider in the course of grading histopathological images. Then, by combining these two features, the classification results were re-assessed utilizing Support Vector Machine (SVM) as a classifier. The assessments were performed on a BreaKHis dataset containing benign and malignant histopathological samples. The average accuracy scores were reported as being 98.2%, 97.2%, 97.8%, and 97.3% in the sub-databases with 40×, 100×, 200×, and 400× magnification factors, respectively. The obtained results showed that the proposed method was quite efficient in histopathological image classification. Despite the relative simplicity of the approach, the obtained results were far superior to previously reported results.