Covid-caps: A capsule network-based framework for identification of covid-19 cases from x-ray images
Abstract Novel Coronavirus disease (COVID-19) has abruptly and undoubtedly changed the
world as we know it at the end of the 2nd decade of the 21st century. COVID-19 is extremely …
world as we know it at the end of the 2nd decade of the 21st century. COVID-19 is extremely …
Capsule networks for image classification: A review
Over the past few years, the computer vision domain has evolved and made a revolutionary
transition from human-engineered features to automated features to address challenging …
transition from human-engineered features to automated features to address challenging …
Early diagnosis of brain tumour mri images using hybrid techniques between deep and machine learning
Cancer is considered one of the most aggressive and destructive diseases that shortens the
average lives of patients. Misdiagnosed brain tumours lead to false medical intervention …
average lives of patients. Misdiagnosed brain tumours lead to false medical intervention …
Covid-fact: A fully-automated capsule network-based framework for identification of covid-19 cases from chest ct scans
The newly discovered Coronavirus Disease 2019 (COVID-19) has been globally spreading
and causing hundreds of thousands of deaths around the world as of its first emergence in …
and causing hundreds of thousands of deaths around the world as of its first emergence in …
Deep autoencoder-based automated brain tumor detection from MRI data
F Demir - Artificial Intelligence-Based Brain-Computer Interface, 2022 - Elsevier
Brain tumor detection from magnetic resonance (MR) image samples is the core way for
radiologists, specialists, and physicians. Artificial intelligence-based MR image classification …
radiologists, specialists, and physicians. Artificial intelligence-based MR image classification …
Hybrid techniques of analyzing mri images for early diagnosis of brain tumours based on hybrid features
Brain tumours are considered one of the deadliest tumours in humans and have a low
survival rate due to their heterogeneous nature. Several types of benign and malignant brain …
survival rate due to their heterogeneous nature. Several types of benign and malignant brain …
Unified approach for accurate brain tumor Multi-Classification and segmentation through fusion of advanced methodologies
This research introduces a unified approach for accurate brain tumor Multi-classification and
segmentation through the fusion of diverse yet complementary methodologies. Brain tumor …
segmentation through the fusion of diverse yet complementary methodologies. Brain tumor …
Computationally optimized brain tumor classification using attention based GoogLeNet-style CNN
Early detection of diseases is a crucial step towards patient healthcare and recovery,
especially, for low survival rate diseases like brain cancer. In recent years, deep learning …
especially, for low survival rate diseases like brain cancer. In recent years, deep learning …
Ct-caps: Feature extraction-based automated framework for covid-19 disease identification from chest ct scans using capsule networks
The global outbreak of the novel corona virus (COVID-19) disease has drastically impacted
the world and led to one of the most challenging crisis across the globe since World War II …
the world and led to one of the most challenging crisis across the globe since World War II …
Application of density-based clustering algorithm and capsule network to performance monitoring of antimony flotation process
L Cen, Y Wu, J Hu, E **a, Y **e, Z Tang - Minerals Engineering, 2022 - Elsevier
This paper presents an application of the capsule network to predict the antimony grade of
pulp in the roughing cell of an antimony flotation plant in the Hunan Province, China. In this …
pulp in the roughing cell of an antimony flotation plant in the Hunan Province, China. In this …