Medical Image Denoising using Deep Convolutional Autoencoders for Ultrasound

This paper describes a new strategy for medical image deaning based on the Deep Convolutional Autoencoders (DCAE) with focus on ultrasound modality. There are several types of noise including speckle, Gaussian and salt-and-pepper noise, which interfere with the ultrasound image and complicate the diagnostic interpretation of the image. To this end, the authors suggest a model that utilizes DCAE and deep learning to demine noise while maintaining the structural features required for diagnosis. Gaussian and median filtering, which are the typical filters for denoising, are used as benchmarks for comparison; the current state-of-the-art deep learning models are also included in the comparison list. This reveals that the proposed DCAE has better performance than these methods through higher PSNR, SSIM, and lower MSE. Also, the comparative analysis of customers subjective perception of the image quality has revealed that the denoised images created with the help of the DCAE have better visualization. This successfully establishes a suitable performance when the model is faced with various forms of noise across ultrasound imaging conditions as evidenced in the model presented above. In addition, owing to its ability to achieve fast-inference time and flexibility in terms of deployment, the model can be implemented for real-time clinical use. In conclusion, the utilization of DCAE-based approach considered in the current paper can be viewed as a viable method that can be used in the further enhancement of the quality of the ultrasound images together with the potential for increasing the levels of diagnostic accuracy as well as delivering better quality of care to patients in their recovery process within medical practice.