🧠 Gabor Wavelet-based Image Classification using Convolutional Neural Networks (CNNs)

This repository presents a deep learning-based approach to improve image classification performance by leveraging Gabor wavelet representations of images. The core idea is to train multiple CNN models using both original and Gabor-transformed images and perform ensemble decision fusion to enhance classification accuracy.

---

📌 Project Summary

We apply this approach to classify chest X-ray images for pneumonia detection, combining traditional CNN architectures with directionally localized features extracted via Gabor filters.

🔍 Motivation

• Improving diagnostic tools for pneumonia detection using X-rays.
• Exploiting the spatial-frequency sensitivity of Gabor wavelets.
• Combining signal processing and deep learning to improve generalization.

---

🌐 Methodology

🌀 Gabor Wavelets & Filters

• Gabor wavelets are complex exponentials modulated by Gaussians, sensitive to frequency and orientation.
• Gabor filters act as localized, orientation-tuned edge detectors.

🔄 Image Transformation Pipeline

• For each input image:
  1. Apply Gabor filters at 8 different orientations.
  2. Generate 8 Gabor-transformed images.
  3. Train 9 CNNs (1 with the original image + 8 with Gabor-transformed variants).

🧮 Decision Fusion

• Final prediction is made by aggregating the output probabilities from all CNNs using the Sum of Probabilities (SMP) Rule:
• A custom threshold of 0.4 is used for final classification.

---

📊 Dataset

• Source: Pediatric chest X-ray dataset from Guangzhou Women and Children’s Medical Center.
• Size: 5,856 JPEG images
  • Training: 5,216
  • Validation: 16
  • Testing: 624
• Classes: Pneumonia, Normal

---

🏗️ CNN Architecture

Standard convolutional architecture with ReLU activations and max-pooling. Each CNN is trained independently on either the raw image or a specific Gabor-transformed version.

---

🧠 Trained Models

The repository includes a models/ folder, that contains the 9 trained CNN models used in this project. These models are saved as final_model_0.h5 to final_model_9.h5.

Each model corresponds to a CNN trained on a specific representation of the input image:

• final_model_0.h5 is trained on the original image (no Gabor transformation).
• final_model_1.h5 to final_model_8.h5 are trained on Gabor-transformed images, each filtered at a specific orientation.

📐 Orientation Mapping

Model File	Description	Gabor Filter Orientation
final_model_0.h5	Trained on original input image	None
final_model_1.h5	Trained on Gabor-transformed image	0°
final_model_2.h5	Trained on Gabor-transformed image	22.5°
final_model_3.h5	Trained on Gabor-transformed image	45°
final_model_4.h5	Trained on Gabor-transformed image	67.5°
final_model_5.h5	Trained on Gabor-transformed image	90°
final_model_6.h5	Trained on Gabor-transformed image	112.5°
final_model_7.h5	Trained on Gabor-transformed image	135°
final_model_8.h5	Trained on Gabor-transformed image	157.5°

These models are used during inference for ensemble prediction via decision fusion, aggregating the outputs of all 9 models to produce the final classification result.

---

📈 Results

Model	Accuracy	Precision	Recall	F1-Score
Single CNN (Raw)	90.06%	92.27%	91.79%	0.92
Ensemble (Fused)	92.47%	90.73%	97.94%	0.94

The ensemble model significantly improves recall while maintaining high precision.

---

🔭 Future Work

• Try alternative wavelet families for image decomposition.
• Explore other CNN variants (e.g., ResNet, EfficientNet).
• Investigate wavelet packet decomposition for richer representations.

---

📚 References

1. Granlund, G. H. (1978). In Search of a General Picture Processing Operator. Computer Graphics and Image Processing, 8(2), 155–173. https://doi.org/10.1016/0146-664X(78)90047-3

2. Serte, S., & Demirel, H. (2019). Gabor wavelet-based deep learning for skin lesion classification. Computers in Biology and Medicine, 113, 103423. https://doi.org/10.1016/j.compbiomed.2019.103423

---

👨‍🎓 Author

Rajdeep Pathak
Department of Mathematics,
Indian Institute of Technology Hyderabad
📅 May 6, 2025