A Lightweight Hybrid Gabor Deep Learning Approach and its Application to Medical Image Classification

Deep learning has revolutionized image analysis, but its applications are limited by the need for large datasets and high computational resources. Hybrid approaches that combine domain-specific, universal feature extractor with learnable neural networks offer a promising balance of efficiency and accuracy. This paper presents a hybrid model integrating a Gabor filter bank front-end with compact neural networks for efficient feature extraction and classification. Gabor filters, inherently bandpass, extract early-stage features with spatially shifted filters covering the frequency plane to balance spatial and spectral localization. We introduce separate channels capturing low- and high-frequency components to enhance feature representation while maintaining efficiency. The approach reduces trainable parameters and training time while preserving accuracy, making it suitable for resource-constrained environments. Compared to MobileNetV2 and EfficientNetB0, our model trains approximately 4-6 x faster on average while using fewer parameters and FLOPs. We compare it to pretrained networks used as feature extractors, lightweight fine-tuned models, and classical descriptors (HOG, LBP). It achieves competitive results with faster training and reduced computation. The hybrid model uses only around 0.60 GFLOPs and 0.34 M parameters, and we apply statistical significance testing (ANOVA, paired t-tests) to validate performance gains. Inference takes 0.01-0.02 s per image, up to 15 x faster than EfficientNetB0 and 8 x faster than MobileNetV2. Grad-CAM visualizations confirm localized attention on relevant regions. This work highlights integrating traditional features with deep learning to improve efficiency for resource-limited applications. Future work will address color fusion, robustness to noise, and automated filter optimization.