Medical imaging is a rich source of invaluable information necessary for clinical judgements.
However, the analysis of those exams is not a trivial assignment. In recent times, the use of …

Lung cancer is the primary reason of cancer deaths worldwide, and the percentage of death
rate is increasing step by step. There are chances of recovering from lung cancer by …

Lung cancer is one of the malignant tumors with high morbidity and mortality worldwide.
Among them, non-small cell lung cancer accounts for about 85% of all lung cancers. In the …

Abstract Non-Small Cell Lung Cancer (NSCLC) is a major lung cancer type. Proper
diagnosis depends mainly on tumor staging and grading. Pathological prognosis often faces …

Purpose: SARS-COV-2, a severe acute respiratory syndrome, has caused more than 1
million to be infected worldwide. Corona Virus Disease, known as COVID-19, has cases that …

The Internet of Things (IoT) has been influential in predicting major diseases in current
practice. The deep learning (DL) technique is vital in monitoring and controlling the …

The current methods of auto-segmenting medical images are limited due to insufficient and
ambiguous pathonmorphological labeling. In clinical practice, rough classification labels …

In computed tomography, state-of-the-art reconstruction is based on neural network (NN)
algorithms. However, NN reconstruction algorithms can be not robust to small noise-like …

Deep learning attempts medical image denoising either by directly learning the noise
present or via first learning the image content. We observe that residual learning (RL) often …

Medical image acquisition is often intervented by unwanted noise that corrupts the
information content. This paper introduces an unsupervised medical image denoising …