An Image Denoising Scheme Remove Unwanted Pixel Using NLM with Sprint Deep Learning Network
Keywords:
Image noise, Machine learning, Deep Learning networks, non-local means algorithm, Image classification, SegmentationAbstract
An automatic image processing system is vital for detecting life-threatening diseases like tumor diagnosis. Machine learning is significant in processing medical images to detect the diseases' signs and symptoms. The essential need for image processing is the earlier detection of diseases. But due to the image complexities, the inefficiency of the conventional methods delivers poor performance in detecting the damaged cell's shape, extraction, exact size, and location. The primary goal of achieving an enhanced automatic system for preprocessing, classifying, segmenting, detecting, and sample recognition remains a challenging task. To overcome this, we proposed an improved SPRINT algorithm with NLM (Non-Local Mean filtering) algorithm. The proposed mechanism computes the original images to remove the noises, and the non-local filtering algorithm considers the high-extent redundancy of the normal images. As a result, the input images are processed using the weighted average value of the entire pixel to obtain noise-free or noise-less pixel images. Additionally, the SPRINT algorithm applies the minimum description length principle for achieving accuracy in the expected details. It contains an attribute table and histogram for holding the indexing of data records, class identification, and attribute values. Finally, our enhanced SPRINT can solve this trouble by preserving the fine details of an image while denoising. To evaluate the performance of the proposed system, a comparison work is carried out between the enhanced SPRINT algorithm with a conventional neural network (CNN) [12] and deep learning-based patch label denoising methods (LossDiff) [13]. The proposed SPRINT algorithm achieves 97% accuracy, which is far better than the CNN with 91% and LossDiff with 85% accuracy.
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