Breast cancer is one of the most prevalent malignant tumors among women worldwide, underscoring the urgent need for early and accurate diagnosis to reduce mortality. To address this, A Multi-Feature Fusion Classification Network (MFF-ClassificationNet) is proposed for breast histopathological image classification. The network adopts a two-branch parallel architecture, where a convolutional neural network captures local details and a Transformer models global dependencies. Their features are deeply integrated through a Multi-Feature Fusion module, which incorporates a Convolutional Block Attention Module—Squeeze and Excitation (CBAM-SE) fusion block combining convolutional block attention, squeeze-and-excitation mechanisms, and a residual inverted multilayer perceptron to enhance fine-grained feature representation and category-specific lesion characterization. Experimental evaluations on the BreakHis dataset achieved accuracies of 98.30%, 97.62%, 98.81%, and 96.07% at magnifications of 40×, 100×, 200×, and 400×, respectively, while an accuracy of 97.50% was obtained on the BACH dataset. These results confirm that integrating local and global features significantly strengthens the model’s ability to capture multi-scale and context-aware information, leading to superior classification performance. Overall, MFF-ClassificationNet surpasses conventional single-path approaches and provides a robust, generalizable framework for advancing computer-aided diagnosis of breast cancer.

Fourier ptychographic microscopy (FPM) achieves high-resolution reconstruction by stitching multi-angle illuminated images in the frequency domain, but its traditional full sampling mode requires activating a large number of LED sources, leading to data redundancy and excessive time costs. To address these issues, this paper proposes an illumination optimization strategy based on regional energy sensitivity (RES) and LED array sub-region division, termed RES-FPM, to improve the imaging efficiency of FPM. The method first constructs a frequency-domain sampling window based on the initial high-resolution spectrum map and generates the spatial response map corresponding to each LED through inverse Fourier transform. It then calculates the degree of influence of each LED on the image’s regional energy (RES score). To avoid spatial non-uniformity caused by direct global ranking-based LED selection, this work further divides the LED array into spatial sub-regions. Within each sub-region, several representative LEDs are selected based on their local energy contribution, achieving a spatial-energy-balanced co-optimization. Experimental results demonstrate that, while maintaining spectral coverage and image reconstruction quality, RES-FPM reduces the required image acquisitions by ∼54.4%, yet preserves reconstruction performance comparable to traditional FPM sampling. This significantly lowers computational costs for both data acquisition and reconstruction while improving efficiency. Additionally, it exhibits strong robustness and interpretability when dealing with complex sample structural variations.