Mammographic Image Classification Based on KAN-CBAM

With the fast enhancement of deep learning, research on automatic detection of breast tumors is becoming increasingly in-depth. However, traditional CNNs’ linear kernel has difficulty not only in capturing the nonlinear combination of low-frequency structures and high-frequency details but also in fully exploring the nonlinear discriminative features in medical images. Furthermore, the current models used for breast tumor detection have complex structures and slow inference speeds. Therefore, this study solves the linear kernel problem and improves the model inference speed by using a lightweight mammographic image classification method based on KAN-CBAM to speed up breast cancer diagnosis. The proposed method introduces the KAN convolution module, which embeds a learnable B-spline activation function into the convolution kernel. This scheme improves the capability of the proposed method to capture nonlinear features and improves its capacity to fit complex, nonlinear distributions. Moreover, the proposed method combines the CBAM attention mechanism to screen key semantic channels through channel attention and then uses spatial attention to locate lesion areas, achieving "channel- space" dual feature recalibration, further improving the attention to key features, and achieving more accurate classification in complex and variable medical images. We evaluated the proposed method on the mammographic image datasets DDSM, INbreast, and MIAS to verify its performance. The results prove that KAN-CBAM models have higher adaptation to diverse dataset scales, efficiently acquiring major lesion parts and nonlinear discriminatory features in mammographic images. Meaningful and great enhancements were seen in different metrics such as accuracy, F1-score, AUC, precision, and recall, demonstrating extensively improved model strength and generalization capability.