Deep learning techniques for liver and liver tumor segmentation: A review
Liver and liver tumor segmentation from 3D volumetric images has been an active research
area in the medical image processing domain for the last few decades. The existence of …
area in the medical image processing domain for the last few decades. The existence of …
Machine learning based liver disease diagnosis: A systematic review
The computer-based approach is required for the non-invasive detection of chronic liver
diseases that are asymptomatic, progressive, and potentially fatal in nature. In this study, we …
diseases that are asymptomatic, progressive, and potentially fatal in nature. In this study, we …
Breast cancer classification from histopathological images using patch-based deep learning modeling
Accurate detection and classification of breast cancer is a critical task in medical imaging
due to the complexity of breast tissues. Due to automatic feature extraction ability, deep …
due to the complexity of breast tissues. Due to automatic feature extraction ability, deep …
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 …
TPCNN: two-path convolutional neural network for tumor and liver segmentation in CT images using a novel encoding approach
Automatic liver and tumour segmentation in CT images are crucial in numerous clinical
applications, such as postoperative assessment, surgical planning, and pathological …
applications, such as postoperative assessment, surgical planning, and pathological …
Modified U-Net for liver cancer segmentation from computed tomography images with a new class balancing method
YA Ayalew, KA Fante, MA Mohammed - BMC Biomedical Engineering, 2021 - Springer
Background Liver cancer is the sixth most common cancer worldwide. It is mostly diagnosed
with a computed tomography scan. Nowadays deep learning methods have been used for …
with a computed tomography scan. Nowadays deep learning methods have been used for …
RMS-UNet: Residual multi-scale UNet for liver and lesion segmentation
Precise segmentation is in demand for hepatocellular carcinoma or metastasis clinical
diagnosis due to the heterogeneous appearance and diverse anatomy of the liver on …
diagnosis due to the heterogeneous appearance and diverse anatomy of the liver on …
List of deep learning models
Deep learning (DL) algorithms have recently emerged from machine learning and soft
computing techniques. Since then, several deep learning (DL) algorithms have been …
computing techniques. Since then, several deep learning (DL) algorithms have been …
OP-convNet: a patch classification-based framework for CT vertebrae segmentation
Accurate vertebrae segmentation from medical images plays an important role in clinical
tasks of surgical planning, diagnosis, kyphosis, scoliosis, degenerative disc disease …
tasks of surgical planning, diagnosis, kyphosis, scoliosis, degenerative disc disease …
SVseg: Stacked sparse autoencoder-based patch classification modeling for vertebrae segmentation
Precise vertebrae segmentation is essential for the image-related analysis of spine
pathologies such as vertebral compression fractures and other abnormalities, as well as for …
pathologies such as vertebral compression fractures and other abnormalities, as well as for …