Transattunet: Multi-level attention-guided u-net with transformer for medical image segmentation
Accurate segmentation of organs or lesions from medical images is crucial for reliable
diagnosis of diseases and organ morphometry. In recent years, convolutional encoder …
diagnosis of diseases and organ morphometry. In recent years, convolutional encoder …
[HTML][HTML] MEEDNets: Medical image classification via ensemble bio-inspired evolutionary DenseNets
Inspired by the biological evolution, this paper proposes an evolutionary synthesis
mechanism to automatically evolve DenseNet towards high sparsity and efficiency for …
mechanism to automatically evolve DenseNet towards high sparsity and efficiency for …
Parameter-free similarity-aware attention module for medical image classification and segmentation
Automatic classification and segmentation of medical images play essential roles in
computer-aided diagnosis. Deep convolutional neural networks (DCNNs) have shown their …
computer-aided diagnosis. Deep convolutional neural networks (DCNNs) have shown their …
Potential diagnostic application of a novel deep learning-based approach for COVID-19
COVID-19 is a highly communicable respiratory illness caused by the novel coronavirus
SARS-CoV-2, which has had a significant impact on global public health and the economy …
SARS-CoV-2, which has had a significant impact on global public health and the economy …
Self-supervised monocular depth estimation using hybrid transformer encoder
SJ Hwang, SJ Park, JH Baek, B Kim - IEEE Sensors Journal, 2022 - ieeexplore.ieee.org
Depth estimation using monocular camera sensors is an important technique in computer
vision. Supervised monocular depth estimation requires a lot of data acquired from depth …
vision. Supervised monocular depth estimation requires a lot of data acquired from depth …
Ema-vio: Deep visual–inertial odometry with external memory attention
Accurate and robust localization is a fundamental need for mobile agents. Visual–inertial
odometry (VIO) algorithms exploit the information from the camera and inertial sensors to …
odometry (VIO) algorithms exploit the information from the camera and inertial sensors to …
Semi-supervised modified-UNet for lung infection image segmentation
AK Upadhyay, AK Bhandari - IEEE Transactions on Radiation …, 2023 - ieeexplore.ieee.org
Automatic lung infection segmentation in computed tomography (CT) scans can offer great
assistance in radiological diagnosis by improving accuracy and reducing time required for …
assistance in radiological diagnosis by improving accuracy and reducing time required for …
Deep convolutional neural network with fusion strategy for skin cancer recognition: model development and validation
CK Juan, YH Su, CY Wu, CS Yang, CH Hsu… - Scientific reports, 2023 - nature.com
We aimed to develop an accurate and efficient skin cancer classification system using deep-
learning technology with a relatively small dataset of clinical images. We proposed a novel …
learning technology with a relatively small dataset of clinical images. We proposed a novel …
An improved hawks optimizer based learning algorithms for cardiovascular disease prediction
AS Kumar, R Rekha - Biomedical Signal Processing and Control, 2023 - Elsevier
Abstract Earlier Cardiovascular Disease (CVD) prediction is difficult and the prediction
complexity is higher due to the lack of intelligent models. This research proposes a stacked …
complexity is higher due to the lack of intelligent models. This research proposes a stacked …
Semi-supervised multi-view fusion for identifying CAP and COVID-19 with unlabeled CT images
Recently, under the condition of reducing nucleic acid testing for COVID-19 in large
population, the computer-aided diagnosis with the chest computed tomography (CT) image …
population, the computer-aided diagnosis with the chest computed tomography (CT) image …