Medical image segmentation using deep semantic-based methods: A review of techniques, applications and emerging trends
Semantic-based segmentation (Semseg) methods play an essential part in medical imaging
analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is …
analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is …
[HTML][HTML] A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities
Artificial Intelligence (AI) solutions have been widely used in healthcare, and recent
developments in deep neural networks have contributed to significant advances in medical …
developments in deep neural networks have contributed to significant advances in medical …
Automated detection and forecasting of covid-19 using deep learning techniques: A review
Abstract In March 2020, the World Health Organization (WHO) declared COVID-19 a global
epidemic, caused by the SARS-CoV-2 virus. Initially, COVID-19 was diagnosed using real …
epidemic, caused by the SARS-CoV-2 virus. Initially, COVID-19 was diagnosed using real …
Data and model aggregation for radiomics applications: Emerging trend and open challenges
Radiomics is a quantitative approach to analyzing medical multi-layered images in
combination with molecular, genetic and clinical information, which has evidenced very …
combination with molecular, genetic and clinical information, which has evidenced very …
RiceNet: A deep convolutional neural network approach for classification of rice varieties
The cultivation of desired grain varieties holds immense significance as about 67% of the
world's population is associated with the agriculture sector. Unknowingly sowing the wrong …
world's population is associated with the agriculture sector. Unknowingly sowing the wrong …
Hybrid CNN-Transformer model for medical image segmentation with pyramid convolution and multi-layer perceptron
Abstract Vision Transformer (ViT) has emerged as a potential alternative to convolutional
neural networks for large datasets. However, applying ViT directly to medical image …
neural networks for large datasets. However, applying ViT directly to medical image …
Dermosegdiff: A boundary-aware segmentation diffusion model for skin lesion delineation
A Bozorgpour, Y Sadegheih, A Kazerouni… - … Workshop on PRedictive …, 2023 - Springer
Skin lesion segmentation plays a critical role in the early detection and accurate diagnosis of
dermatological conditions. Denoising Diffusion Probabilistic Models (DDPMs) have recently …
dermatological conditions. Denoising Diffusion Probabilistic Models (DDPMs) have recently …
A survey on deep learning in COVID-19 diagnosis
According to the World Health Organization statistics, as of 25 October 2022, there have
been 625,248,843 confirmed cases of COVID-19, including 65,622,281 deaths worldwide …
been 625,248,843 confirmed cases of COVID-19, including 65,622,281 deaths worldwide …
A pyramid input augmented multi-scale CNN for GGO detection in 3D lung CT images
This paper proposes a new convolutional neural network (CNN) with multi-scale processing
for detecting ground-glass opacity nodules (GGO) in 3D computed tomography (CT) images …
for detecting ground-glass opacity nodules (GGO) in 3D computed tomography (CT) images …
CHSNet: Automatic lesion segmentation network guided by CT image features for acute cerebral hemorrhage
B Xu, Y Fan, J Liu, G Zhang, Z Wang, Z Li… - Computers in Biology …, 2023 - Elsevier
Stroke is a cerebrovascular disease that can lead to severe sequelae such as hemiplegia
and mental retardation with a mortality rate of up to 40%. In this paper, we proposed an …
and mental retardation with a mortality rate of up to 40%. In this paper, we proposed an …