Deep learning models and traditional automated techniques for brain tumor segmentation in MRI: a review

P Jyothi, AR Singh - Artificial intelligence review, 2023 - Springer
Brain is an amazing organ that controls all activities of a human. Any abnormality in the
shape of anatomical regions of the brain needs to be detected as early as possible to reduce …

Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review

A Shoeibi, M Khodatars, M Jafari, P Moridian… - Computers in Biology …, 2021 - Elsevier
Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor
problems for people with a detrimental effect on the functioning of the nervous system. In …

A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images

J Rasheed, AA Hameed, C Djeddi, A Jamil… - Interdisciplinary …, 2021 - Springer
Abstract Corona virus disease (COVID-19) acknowledged as a pandemic by the WHO and
mankind all over the world is vulnerable to this virus. Alternative tools are needed that can …

Generative adversarial networks and its applications in the biomedical image segmentation: a comprehensive survey

A Iqbal, M Sharif, M Yasmin, M Raza, S Aftab - International Journal of …, 2022 - Springer
Recent advancements with deep generative models have proven significant potential in the
task of image synthesis, detection, segmentation, and classification. Segmenting the medical …

BiO-Net: learning recurrent bi-directional connections for encoder-decoder architecture

T **ang, C Zhang, D Liu, Y Song, H Huang… - … Image Computing and …, 2020 - Springer
U-Net has become one of the state-of-the-art deep learning-based approaches for modern
computer vision tasks such as semantic segmentation, super resolution, image denoising …

Review of deep learning approaches for the segmentation of multiple sclerosis lesions on brain MRI

C Zeng, L Gu, Z Liu, S Zhao - Frontiers in Neuroinformatics, 2020 - frontiersin.org
In recent years, there have been multiple works of literature reviewing methods for
automatically segmenting multiple sclerosis (MS) lesions. However, there is no literature …

Multiple sclerosis lesion analysis in brain magnetic resonance images: techniques and clinical applications

Y Ma, C Zhang, M Cabezas, Y Song… - IEEE Journal of …, 2022 - ieeexplore.ieee.org
Multiple sclerosis (MS) is a chronic inflammatory and degenerative disease of the central
nervous system, characterized by the appearance of focal lesions in the white and gray …

A survey of deep learning models in medical therapeutic areas

A Nogales, AJ Garcia-Tejedor, D Monge… - Artificial intelligence in …, 2021 - Elsevier
Artificial intelligence is a broad field that comprises a wide range of techniques, where deep
learning is presently the one with the most impact. Moreover, the medical field is an area …

[HTML][HTML] BTS-GAN: computer-aided segmentation system for breast tumor using MRI and conditional adversarial networks

IU Haq, H Ali, HY Wang, L Cui, J Feng - Engineering Science and …, 2022 - Elsevier
Breast tumor is one of the most prominent indicators for the diagnosis of breast cancer. The
precise segmentation of tumors is crucial for enhancing the accuracy of breast cancer …

US2Mask: Image-to-mask generation learning via a conditional GAN for cardiac ultrasound image segmentation

G Wang, M Zhou, X Ning, P Tiwari, H Zhu… - Computers in Biology …, 2024 - Elsevier
Cardiac ultrasound (US) image segmentation is vital for evaluating clinical indices, but it
often demands a large dataset and expert annotations, resulting in high costs for deep …