Deep learning for medical image cryptography: A comprehensive review
K Lata, LR Cenkeramaddi - Applied Sciences, 2023 - mdpi.com
Electronic health records (EHRs) security is a critical challenge in the implementation and
administration of Internet of Medical Things (IoMT) systems within the healthcare sector's …
administration of Internet of Medical Things (IoMT) systems within the healthcare sector's …
Machine learning and deep learning techniques for driver fatigue and drowsiness detection: a review
There are several factors for vehicle accidents during driving such as drivers' negligence,
drowsiness, and fatigue. These accidents can be avoided, if drivers are warned in time …
drowsiness, and fatigue. These accidents can be avoided, if drivers are warned in time …
Vision transformers in image restoration: A survey
The Vision Transformer (ViT) architecture has been remarkably successful in image
restoration. For a while, Convolutional Neural Networks (CNN) predominated in most …
restoration. For a while, Convolutional Neural Networks (CNN) predominated in most …
Brain tumor classification using hybrid single image super-resolution technique with ResNext101_32× 8d and VGG19 pre-trained models
High-quality images acquired from medical devices can be utilized to aid diagnosis and
detection of various diseases. However, such images can be very expensive to acquire and …
detection of various diseases. However, such images can be very expensive to acquire and …
Transfer learning-based multi-scale denoising convolutional neural network for prostate cancer detection
KT Chui, BB Gupta, HR Chi, V Arya, W Alhalabi… - Cancers, 2022 - mdpi.com
Simple Summary To enhance the automatic diagnosis of the prostate cancer using machine
learning algorithm, we modify the design of convolutional neural network to support multi …
learning algorithm, we modify the design of convolutional neural network to support multi …
[PDF][PDF] An efficient medical image deep fusion model based on convolutional neural networks
Medical image fusion is considered the best method for obtaining one image with rich
details for efficient medical diagnosis and therapy. Deep learning provides a high …
details for efficient medical diagnosis and therapy. Deep learning provides a high …
Evae-net: An ensemble variational autoencoder deep learning network for covid-19 classification based on chest x-ray images
The COVID-19 pandemic has had a significant impact on many lives and the economies of
many countries since late December 2019. Early detection with high accuracy is essential to …
many countries since late December 2019. Early detection with high accuracy is essential to …
A novel autoencoder based feature independent GA optimised XGBoost classifier for IoMT malware detection
Abstract The Internet of Medical Things (IoMT) has a network of interconnected medical
devices to capture patients' health metrics and store them in a centralized server for analysis …
devices to capture patients' health metrics and store them in a centralized server for analysis …
An adaptive watershed segmentation based medical image denoising using deep convolutional neural networks
A Annavarapu, S Borra - Biomedical Signal Processing and Control, 2024 - Elsevier
Until today, researchers have introduced a range of methodologies to decrease the noise
effect on medical images. In the proposed approach, an adapted deep convolutional neural …
effect on medical images. In the proposed approach, an adapted deep convolutional neural …
Ensemble of Autoencoders for Anomaly Detection in Biomedical Data: A Narrative Review
In the context of biomedical data, an anomaly could refer to a rare or new type of disease, an
aberration from normal behavior, or an unexpected observation requiring immediate …
aberration from normal behavior, or an unexpected observation requiring immediate …