Deep learning techniques for liver and liver tumor segmentation: A review

S Gul, MS Khan, A Bibi, A Khandakar, MA Ayari… - Computers in Biology …, 2022 - Elsevier
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 …

Machine learning based liver disease diagnosis: A systematic review

RA Khan, Y Luo, FX Wu - Neurocomputing, 2022 - Elsevier
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 …

Breast cancer classification from histopathological images using patch-based deep learning modeling

I Hirra, M Ahmad, A Hussain, MU Ashraf… - IEEE …, 2021 - ieeexplore.ieee.org
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 …

A lightweight convolutional neural network model for liver segmentation in medical diagnosis

M Ahmad, SF Qadri, S Qadri, IA Saeed… - Computational …, 2022 - Wiley Online Library
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 …

TPCNN: two-path convolutional neural network for tumor and liver segmentation in CT images using a novel encoding approach

A Aghamohammadi, R Ranjbarzadeh, F Naiemi… - Expert Systems with …, 2021 - Elsevier
Automatic liver and tumour segmentation in CT images are crucial in numerous clinical
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 …

RMS-UNet: Residual multi-scale UNet for liver and lesion segmentation

RA Khan, Y Luo, FX Wu - Artificial Intelligence in Medicine, 2022 - Elsevier
Precise segmentation is in demand for hepatocellular carcinoma or metastasis clinical
diagnosis due to the heterogeneous appearance and diverse anatomy of the liver on …

List of deep learning models

A Mosavi, S Ardabili, AR Varkonyi-Koczy - International conference on …, 2019 - Springer
Deep learning (DL) algorithms have recently emerged from machine learning and soft
computing techniques. Since then, several deep learning (DL) algorithms have been …

OP-convNet: a patch classification-based framework for CT vertebrae segmentation

SF Qadri, L Shen, M Ahmad, S Qadri, SS Zareen… - IEEE …, 2021 - ieeexplore.ieee.org
Accurate vertebrae segmentation from medical images plays an important role in clinical
tasks of surgical planning, diagnosis, kyphosis, scoliosis, degenerative disc disease …

SVseg: Stacked sparse autoencoder-based patch classification modeling for vertebrae segmentation

SF Qadri, L Shen, M Ahmad, S Qadri, SS Zareen… - Mathematics, 2022 - mdpi.com
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 …