Wavelet-Enhanced Deformable Convolutional Network for Breast Cancer Classification in High-Resolution Histopathology Images

Abstract

The limitations of deep learning methods in processing high-resolution inputs can impact the accuracy and efficiency of their results. This study presents a new architectural framework that combines wavelet-based preprocessing with deformable convolutional networks to classify high-resolution histopathological images. Our methodology utilizes multi-resolution wavelet decomposition for efficient feature extraction which maintains diagnostically significant information. This improvement is augmented by deformable convolutions, which improve robustness against geometric transformations of the inputs. Empirical evaluation on the BreaKHis data set shows an image-level accuracy of 96.47% and a patient-level accuracy of 96.55% at 200× magnification. The architecture consistently performs well across different magnification levels, with particular efficiency at higher resolutions where detailed morphological features are essential for accurate diagnosis. Ablation studies support our key architectural contributions, including reduced computational complexity through wavelet-based feature extraction, improved geometric invariance via deformable convolutions, and better classification performance than conventional methods. These findings suggest significant potential for improving diagnostic workflows in clinical settings where pathological expertise may be limited.