Classification of retinal images based on convolutional neural network
Automatic detection of maculopathy disease is a very important step to achieve high‐
accuracy results for the early discovery of the disease to help ophthalmologists to treat …
accuracy results for the early discovery of the disease to help ophthalmologists to treat …
[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 …
Automated diagnosis of EEG abnormalities with different classification techniques
Automatic seizure detection and prediction using clinical Electroencephalograms (EEGs)
are challenging tasks due to factors such as low Signal-to-Noise Ratios (SNRs), high …
are challenging tasks due to factors such as low Signal-to-Noise Ratios (SNRs), high …
[PDF][PDF] Deep cnn model for multimodal medical image denoising
In the literature, numerous techniques have been employed to decrease noise in medical
image modalities, including X-Ray (XR), Ultrasonic (Us), Computed Tomography (CT) …
image modalities, including X-Ray (XR), Ultrasonic (Us), Computed Tomography (CT) …
Proposed neural SAE-based medical image cryptography framework using deep extracted features for smart IoT healthcare applications
Image cryptography based on chaos algorithms is widely employed in modern security
systems in telemedicine Internet of Things (IoT) applications. One of the main drawbacks of …
systems in telemedicine Internet of Things (IoT) applications. One of the main drawbacks of …
Medical image enhancement algorithms using deep learning-based convolutional neural network
C Ghandour, W El-Shafai, S El-Rabaie - Journal of Optics, 2023 - Springer
The reliable detection of diseases using deep learning-based medical image fusion (DLMIF)
is a common practice nowadays. The performance of DLMIF depends on the features …
is a common practice nowadays. The performance of DLMIF depends on the features …
A Hybrid Compressive Sensing and Classification Approach for Dynamic Storage Management of Vital Biomedical Signals
The efficient compression and classification of medical signals, particularly
electroencephalography (EEG) and electrocardiography (ECG) signals in wireless body …
electroencephalography (EEG) and electrocardiography (ECG) signals in wireless body …
Efficient frameworks for EEG epileptic seizure detection and prediction
Seizure detection and prediction are a very hot topics in medical signal processing due to
their importance in automatic medical diagnosis. This paper presents three efficient …
their importance in automatic medical diagnosis. This paper presents three efficient …
Efficient communication and EEG signal classification in wavelet domain for epilepsy patients
SAE El-Gindy, A Hamad, W El-Shafai… - Journal of ambient …, 2021 - Springer
In this paper, we present an approach for the anticipation of electroencephalography (EEG)
seizures using different families of wavelet transform. Different signal attributes are …
seizures using different families of wavelet transform. Different signal attributes are …
SIFT and SURF based feature extraction for the anomaly detection
In this paper, we suggest a way, how to use SIFT and SURF algorithms to extract the image
features for anomaly detection. We use those feature vectors to train various classifiers on a …
features for anomaly detection. We use those feature vectors to train various classifiers on a …