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<p>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 × 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&#xa0;M parameters, and we apply statistical significance testing (ANOVA, paired t-tests) to validate performance gains. Inference takes 0.01–0.02&#xa0;s per image, up to 15 × faster than EfficientNetB0 and 8 × 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月5日

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  • Rayyan Ahmed,
  • Hamza Baali,
  • Abdesselam Bouzerdoum

摘要

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 × 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 × faster than EfficientNetB0 and 8 × 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.