The liver is an important and multifunctional human organ. Early and accurate diagnosis of a
liver tumor can save lives. Computed Tomography (CT) images provide comprehensive …

Medical image analysis requires liver segmentation for liver disease detection and
treatment. Deep learning approaches, particularly liver segmentation, have demonstrated …

Worldwide, more than 1.5 million deaths are occur due to liver cancer every year. The use of
computed tomography (CT) for early detection of liver cancer could save millions of lives per …

Although the use of immune checkpoint inhibitors (ICIs)-targeted agents for unresectable
hepatocellular carcinoma (HCC) is promising, individual response variability exists …

Segmenting the liver and liver tumors in computed tomography (CT) images is an important
step toward quantifiable biomarkers for a computer-aided decision-making system and …

Purpose: This paper proposes a new network framework called EAR-U-Net, which
leverages EfficientNetB4, attention gate, and residual learning techniques to achieve …

This paper proposes a new network framework, which leverages EfficientNetB4, attention
gate, and residual learning techniques to achieve automatic and accurate liver …

An automated diagnostic system leads to a supreme requirement in medical image analysis,
greatly impacting the death rate due to the high spreading rate of liver tumors. However, the …

Liver cancer is the primary reason of death around the globe. Manually detecting the
infected tissues is a challenging and time-consuming task. The computerized methods help …

This study aimed to develop a deep learning-based model to simultaneously perform the
objective response (OR) and tumor segmentation for hepatocellular carcinoma (HCC) …