Breast lesion identification using feature fusion and multiresolution dual-tree complex wavelet transform.

Objective: Breast cancer is considered a significant cause of death among females globally, so there is an urgent need for accurate and computerized methods of diagnosis. The study will develop a robust computerized framework for the identification of breast lesions using multiresolution feature fusion based on the Dual-Tree Complex Wavelet Transform (DT-CWT) applied to histopathological images. Methods: A total of 7909 histopathological images were sourced from the publicly available BreakHis dataset, and 594 clinical samples were acquired from the Department of Radiodiagnosis and Imaging at Graphic Era Institute of Medical Sciences to ensure that variations across breast cancer subtypes were represented. In the proposed framework, the steps included image preprocessing, two-level DT-CWT decomposition using two-dimensional wavelet filters, and feature extraction from the low-frequency sub-bands. Handcrafted features were extracted as Law's texture energy measures, Gabor-based texture descriptors, and statistical textural features, and fused into a comprehensive multiresolution feature set. These fused features have then been used to classify benign and malignant breast lesions in a dual-path convolutional neural network. Results: The experimental assessment showed that the Reverse Biorthogonal (Rbio-2.4) wavelet filter achieved the best classification performance, with a test accuracy of 97.32%, compared to other wavelet filters across various wavelet families. Moreover, the new method showed the highest values for precision, recall, F1 score, Matthews correlation coefficient, and Cohen's kappa, which measure diagnostic performance. Conclusion: The results obtained have confirmed that the designed feature fusion approach using the DT-CWT has superior capability for the precise identification of breast lesions in histopathologic images. By combining multiresolution wavelet analysis, hand-engineered texture features, and CNN-based learning models, the proposed approach has potential applications in healthcare for breast cancer detection.