An Effective Image Denoising Model Using Improved Deep Learning Techniques with Optimization Algorithm
S. Mythili, Selligoundanur Subramaniyam Sivaraju, T. Anuradha, S. Sivarajan · International Journal of Pattern Recognition and Artificial Intelligence · 2025
Physicians and radiologists utilize medical image processing to diagnose diseases. However, medical images can be compromised by noise introduced through various imaging processes, leading to diminished image quality. This degradation manifests as blurred boundaries, suppressed edges, and loss of structural details. Preserving edges and details is crucial for accurate disease diagnosis. Therefore, medical image denoising is essential in aiding physicians with diagnosis. MRI, CT, X-ray, and ultrasound images are examples of common medical image types. To overcome the above problems, this work implemented an Improved Convolutional Neural Network (ICNN) with the Self-Improved Orca Predation Algorithm (SI-OPA) for image denoising for medical image denoising. ICNN has been used to treat noise-distorted medical images. The CNN’s weights are modified by SI-OPA, increasing the precision of denoising. Comprehensive evaluations concern cutting-edge denoising approaches, encompassing conventional, and deep learning-based methods, emphasizing denoising efficacy, computing efficiency, and picture detail retention. Compared to traditional image denoising algorithms, the proposed method offers superior noise suppression, as evidenced by improved accuracy, Structural Similarity (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Standard Deviation (STD) metrics. Additionally, experimental findings support the suggested denoising algorithm’s viability and efficiency. Finally, the findings show that whereas the current approaches only reach 86%, 82%, 78%, and 65% of the study results, the suggested model achieves a high 94%. Accuracy is 91%, SSIM is 0.91, PSNR is 45.75%, and STD metrics is 43.89%.