Medical imaging methods, such as CT scans, MRI scans, X-rays, and ultrasound imaging,
are widely used for diagnosis in the healthcare domain. However, these methods are often …

Deep learning has revolutionized the detection of diseases and is hel** the healthcare
sector break barriers in terms of accuracy and robustness to achieve efficient and robust …

Radiography is one of the important clinical adjuncts for preliminary disease investigation.
The X-ray images are corrupted with inherent quantum noise affecting the performance of …

In medical imaging, denoising is very important for analysis of images, diagnosis and
treatment of diseases. Currently, image denoising methods based on deep learning are …

The agriculture sector contributes significantly to the economic growth of a country.
However, plant diseases are one of the leading causes of crop destruction that decreases …

The reliability of machine learning models can be compromised when trained on low quality
data. Many large-scale medical imaging datasets contain low quality labels extracted from …

Segmentation of 2D images is a fundamental problem for biomedical image analysis. The
most widely used architecture for biomedical image segmentation is U-Net. U-Net introduces …

The complex feature characteristics and low contrast of cancer lesions, a high degree of
inter-class resemblance between malignant and benign lesions, and the presence of …

A typical growth of cells inside tissue is normally known as a nodular entity. Lung nodule
segmentation from computed tomography (CT) images becomes crucial for early lung …

Image denoising performs a prominent role in medical image analysis. In many cases, it can
drastically accelerate the diagnostic process by enhancing the perceptual quality of noisy …