Projects Details

Project Name: Convolutional Autoencoder for Denoising CT Scan Images

                
                  Objective:
                  Develop a convolutional autoencoder with residual skip
                    connections to enhance CT images by removing noise.

Design:

Architecture 1 (Lighter Version)

Focus: Optimized for computational efficiency.

Features: Minimalist architecture with fewer layers and parameters, suitable for environments with limited computational resources.

Performance: Good reconstruction quality with reduced computation time.

Architecture 2 (Pro Version)

Focus: Maximized reconstruction accuracy.

Features: Deeper, more complex architecture with additional layers and skip connections for high-quality denoising.

Performance: Superior reconstruction quality with higher computational demands.

                    
                       Key Achievements:
                      Achieved higher PSNR and SSIM metrics compared to traditional
                        denoising methods like Gaussian and Wiener filters.

                    
                       Outcome:
                      Currently in the process of writing an IEEE conference paper and
                        working on a journal article based on this research.

                    
                       Tools:
                      Python, TensorFlow/Keras

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Projects Details

Project Name: Convolutional Autoencoder for Denoising CT Scan Images

Objective: Develop a convolutional autoencoder with residual skip connections to enhance CT images by removing noise.

Design:

Architecture 1 (Lighter Version)

Focus: Optimized for computational efficiency.

Features: Minimalist architecture with fewer layers and parameters, suitable for environments with limited computational resources.

Performance: Good reconstruction quality with reduced computation time.

Architecture 2 (Pro Version)

Focus: Maximized reconstruction accuracy.

Features: Deeper, more complex architecture with additional layers and skip connections for high-quality denoising.

Performance: Superior reconstruction quality with higher computational demands.

Key Achievements: Achieved higher PSNR and SSIM metrics compared to traditional denoising methods like Gaussian and Wiener filters.

Outcome: Currently in the process of writing an IEEE conference paper and working on a journal article based on this research.

Tools: Python, TensorFlow/Keras