Deep Learning Based Image Reconstruction Algorithm for Enhanced Diagnosis in Medical Imaging

Authors: Akshat Bhutiani

DOI: https://doi.org/10.5281/zenodo.14005235

Short DOI: https://doi.org/g8pmdk

Country: USA

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Abstract: Modern diagnostics heavily rely on medical imaging, and the precision of the diagnosis is directly impacted by the quality of the reconstructed images. This work presents a novel deep learning-based algorithm for image reconstruction that is intended to improve the quality of medical images in order to facilitate more accurate and efficient diagnosis. The suggested algorithm, which addresses common issues in modalities like MRI, CT scans, and X-rays, reconstructs high-resolution images from partial or noisy data by utilizing convolutional neural networks (CNNs) and sophisticated data augmentation techniques. Test results show that by lowering noise and artifacts while keeping a high computational efficiency, the algorithm not only increases lesion detection sensitivity but also improves image clarity. Medical practitioners will be able to diagnose patients more quickly and accurately thanks to the reconstructed images, which demonstrate notable improvements in clinical validation tests. According to this study, deep learning techniques have the potential to completely transform medical imaging by providing automated, faster, and more reliable image reconstruction.

Keywords: Medical imaging, deep learning, artifact removal, noise reduction, convolutional neural network (CNN)

Paper Id: 231387

Published On: 2020-10-07

Published In: Volume 8, Issue 5, September-October 2020

Cite This: Deep Learning Based Image Reconstruction Algorithm for Enhanced Diagnosis in Medical Imaging - Akshat Bhutiani - IJIRMPS Volume 8, Issue 5, September-October 2020. DOI 10.5281/zenodo.14005235

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