D2BOF-COVIDNet: A Framework of Deep Bayesian Optimization and Fusion-Assisted Optimal Deep Features for COVID-19 Classification Using Chest X-ray and MRI …
Background and Objective: In 2019, a corona virus disease (COVID-19) was detected in
China that affected millions of people around the world. On 11 March 2020, the WHO …
China that affected millions of people around the world. On 11 March 2020, the WHO …
COVID-19 classification using chest X-ray images based on fusion-assisted deep Bayesian optimization and Grad-CAM visualization
A Hamza, M Attique Khan, SH Wang… - Frontiers in Public …, 2022 - frontiersin.org
The COVID-19 virus's rapid global spread has caused millions of illnesses and deaths. As a
result, it has disastrous consequences for people's lives, public health, and the global …
result, it has disastrous consequences for people's lives, public health, and the global …
Improve the efficiency of handcrafted features in image retrieval by adding selected feature generating layers of deep convolutional neural networks
G Shamsipour, S Fekri-Ershad, M Sharifi… - Signal, image and video …, 2024 - Springer
Today, with the rapid growth of communication technology and the development of social
networks and smartphones, the amount of data stored by users in the form of images has …
networks and smartphones, the amount of data stored by users in the form of images has …
A Novel CNN pooling layer for breast cancer segmentation and classification from thermograms
Breast cancer is the second most frequent cancer worldwide, following lung cancer and the
fifth leading cause of cancer death and a major cause of cancer death among women. In …
fifth leading cause of cancer death and a major cause of cancer death among women. In …
[HTML][HTML] A novel ensemble CNN model for COVID-19 classification in computerized tomography scans
LF de Jesus Silva, OAC Cortes, JOB Diniz - Results in Control and …, 2023 - Elsevier
COVID-19 is a rapidly spread infectious disease caused by a severe acute respiratory
syndrome that can lead to death in just a few days. Thus, early disease detection can …
syndrome that can lead to death in just a few days. Thus, early disease detection can …
A hybrid attention-based residual Unet for semantic segmentation of brain tumor
Segmenting brain tumors in Magnetic Resonance Imaging (MRI) volumes is challenging
due to their diffuse and irregular shapes. Recently, 2D and 3D deep neural networks have …
due to their diffuse and irregular shapes. Recently, 2D and 3D deep neural networks have …
[HTML][HTML] A systematic review of hyperparameter optimization techniques in Convolutional Neural Networks
Abstract Convolutional Neural Network (CNN) is a prevalent topic in deep learning (DL)
research for their architectural advantages. CNN relies heavily on hyperparameter …
research for their architectural advantages. CNN relies heavily on hyperparameter …
Enhancing an imbalanced lung disease x-ray image classification with the CNN-LSTM model
Lung diseases have a significant impact on respiratory health, causing various symptoms
and posing challenges in diagnosis and treatment. This research presents a methodology …
and posing challenges in diagnosis and treatment. This research presents a methodology …
Multimodal deep learning model for Covid-19 detection
FY Issahaku, X Liu, K Lu, X Fang, SB Danwana… - … Signal Processing and …, 2024 - Elsevier
This study introduces a novel multimodal deep learning model for detecting Covid-19,
uniquely combining chest X-ray images and cough sound features. We incorporated an …
uniquely combining chest X-ray images and cough sound features. We incorporated an …
A deep learning‐based x‐ray imaging diagnosis system for classification of tuberculosis, COVID‐19, and pneumonia traits using evolutionary algorithm
To aid in detection of tuberculosis, researchers have concentrated on developing computer‐
aided diagnostic technologies based on x‐ray imaging. Since it generates noninvasive …
aided diagnostic technologies based on x‐ray imaging. Since it generates noninvasive …