U-net is an image segmentation technique developed primarily for image segmentation
tasks. These traits provide U-net with a high utility within the medical imaging community …

Accurate image segmentation plays a crucial role in medical image analysis, yet it faces
great challenges caused by various shapes, diverse sizes, and blurry boundaries. To …

Machine vision systems based on deep learning play an important role in the industrial
Internet of things (IIoT) and Industry 4.0 applications, especially for product quality …

Accurate skin lesion segmentation in dermoscopic images is crucially important for the early
diagnosis of skin cancer. Nevertheless, the existence of complex interfering factors, such as …

Accurate image segmentation plays a crucial role in medical image analysis, yet it faces
great challenges of various shapes, diverse sizes, and blurry boundaries. To address these …

Optical coherence tomography (OCT) is one of the noninvasive and easy-to-acquire
biomarkers (the thickness of the retinal layers, which is detectable within OCT scans) being …

Data imbalance frequently occurs across multiple sectors, including healthcare, security,
and finance. It substantially increases the difficulty of classification. To tackle the issue of …

Financial institution recommendation systems are being developed as big data applications.
They solve specific problems; in particular, they give a clear idea of the available financial …

In this thesis, the applications of deep learning in the analysis, detection and classification of
medical imaging datasets were studied, with a focus on datasets having a limited sample …

Image segmentation has established itself as a cornerstone of modern medical imaging
practices, achieving state-of-the-art accuracy and playing a pivotal role in critical …