An automated liver tumour segmentation and classification model by deep learning based approaches
S Saha Roy, S Roy, P Mukherjee… - Computer Methods in …, 2023 - Taylor & Francis
Liver cancer is regarded as one of the most common and leading causes of cancer death
around the world. Automatic liver tumour segmentation and classification techniques are …
around the world. Automatic liver tumour segmentation and classification techniques are …
[PDF][PDF] Liver lesion segmentation using deep learning models
An estimated 9.6 million deaths, or one in every six deaths, were attributed to cancer in
2018, making it the second highest cause of death worldwide. Men are more likely to …
2018, making it the second highest cause of death worldwide. Men are more likely to …
Liver tumour segmentation based on ResNet technique
A Sirco, A Almisreb, NM Tahir… - 2022 IEEE 12th …, 2022 - ieeexplore.ieee.org
It is known that the sixth most common cancer worldwide is liver cancer and CT scans are
commonly used to diagnose liver cancer. Hence in this study, deep learning techniques …
commonly used to diagnose liver cancer. Hence in this study, deep learning techniques …
[HTML][HTML] Automatic segmentation of liver tumor in CT images with deep convolutional neural networks
Liver tumors segmentation from computed tomography (CT) images is an essential task for
diagnosis and treatments of liver cancer. However, it is difficult owing to the variability of …
diagnosis and treatments of liver cancer. However, it is difficult owing to the variability of …
Cascaded deep convolutional encoder-decoder neural networks for efficient liver tumor segmentation
Liver and hepatic tumor segmentation remains a challenging problem in Computer
Tomography (CT) images analysis due to its shape variation and vague boundary. The …
Tomography (CT) images analysis due to its shape variation and vague boundary. The …
[HTML][HTML] Automatic liver and tumour segmentation from CT images using Deep learning algorithm
RV Manjunath, K Kwadiki - Results in Control and Optimization, 2022 - Elsevier
The diagnosis and treatment of liver diseases from computed tomography (CT) images is an
indispensable task for segmentation of Liver & its tumours. Due to the uneven presence …
indispensable task for segmentation of Liver & its tumours. Due to the uneven presence …
Computer-aided segmentation of liver lesions in CT scans using cascaded convolutional neural networks and genetically optimised classifier
N Nanda, P Kakkar, S Nagpal - Arabian Journal for Science and …, 2019 - Springer
Abdominal CT scans have been widely studied and researched by medical professionals in
recent years. CT scans have proved effective for the task of detection of liver abnormalities in …
recent years. CT scans have proved effective for the task of detection of liver abnormalities in …
An automatic method for segmentation of liver lesions in computed tomography images using deep neural networks
JDL Araújo, LB da Cruz, JL Ferreira… - Expert Systems with …, 2021 - Elsevier
Liver cancer is one of the major causes of death by cancer. The early detection of lesions in
the liver provides a better chance of treatment and cure of the disease. Computed …
the liver provides a better chance of treatment and cure of the disease. Computed …
Automatic liver tumor segmentation based on multi-level deep convolutional networks and fractal residual network
BC Anil, P Dayananda - IETE Journal of Research, 2023 - Taylor & Francis
The liver is a very important and complex organ in our body. Efficient liver segmentation is a
very important interesting and challenging task in the field of medical image processing. The …
very important interesting and challenging task in the field of medical image processing. The …
A lightweight convolutional neural network model for liver segmentation in medical diagnosis
Liver segmentation and recognition from computed tomography (CT) images is a warm topic
in image processing which is helpful for doctors and practitioners. Currently, many deep …
in image processing which is helpful for doctors and practitioners. Currently, many deep …