Comprehensive survey of machine learning systems for COVID-19 detection
The last two years are considered the most crucial and critical period of the COVID-19
pandemic affecting most life aspects worldwide. This virus spreads quickly within a short …
pandemic affecting most life aspects worldwide. This virus spreads quickly within a short …
[HTML][HTML] Application of artificial intelligence in diagnosing COVID-19 disease symptoms on chest X-rays: A systematic review
J Kufel, K Bargieł, M Koźlik, Ł Czogalik… - … Journal of Medical …, 2022 - ncbi.nlm.nih.gov
This systematic review focuses on using artificial intelligence (AI) to detect COVID-19
infection with the help of X-ray images. Methodology: In January 2022, the authors searched …
infection with the help of X-ray images. Methodology: In January 2022, the authors searched …
Efficient framework for brain tumor detection using different deep learning techniques
The brain tumor is an urgent malignancy caused by unregulated cell division. Tumors are
classified using a biopsy, which is normally performed after the final brain surgery. Deep …
classified using a biopsy, which is normally performed after the final brain surgery. Deep …
Simultaneous super-resolution and classification of lung disease scans
Acute lower respiratory infection is a leading cause of death in developing countries. Hence,
progress has been made for early detection and treatment. There is still a need for improved …
progress has been made for early detection and treatment. There is still a need for improved …
Feasibility study of multi-site split learning for privacy-preserving medical systems under data imbalance constraints in COVID-19, X-ray, and cholesterol dataset
It seems as though progressively more people are in the race to upload content, data, and
information online; and hospitals haven't neglected this trend either. Hospitals are now at the …
information online; and hospitals haven't neglected this trend either. Hospitals are now at the …
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 …
Deep‐learning‐based seizure detection and prediction from electroencephalography signals
FE Ibrahim, HM Emara, W El‐Shafai… - International Journal …, 2022 - Wiley Online Library
Electroencephalography (EEG) is among the main tools used for analyzing and diagnosing
epilepsy. The manual analysis of EEG must be conducted by highly trained clinicians or …
epilepsy. The manual analysis of EEG must be conducted by highly trained clinicians or …
Efficient deep learning models for brain tumor detection with segmentation and data augmentation techniques
Brain tumor is an acute cancerous disease that results from abnormal and uncontrollable
cell division. Brain tumors are classified via biopsy, which is not normally done before the …
cell division. Brain tumors are classified via biopsy, which is not normally done before the …
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 …
Deep learning innovations in diagnosing diabetic retinopathy: The potential of transfer learning and the DiaCNN model
Diabetic retinopathy (DR) is a significant cause of vision impairment, emphasizing the critical
need for early detection and timely intervention to avert visual deterioration. Diagnosing DR …
need for early detection and timely intervention to avert visual deterioration. Diagnosing DR …