[HTML][HTML] Deep learning for pneumonia detection in chest X-ray images: a comprehensive survey
This paper addresses the significant problem of identifying the relevant background and
contextual literature related to deep learning (DL) as an evolving technology in order to …
contextual literature related to deep learning (DL) as an evolving technology in order to …
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 …
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 …
CNN Models Using Chest X-Ray Images for COVID-19 Detection: A Survey.
The COVID-19 pandemic, which began in 2019, has spread globally, causing substantial
human suffering and economic disruption. A collaborative global effort is essential to combat …
human suffering and economic disruption. A collaborative global effort is essential to combat …
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 …
HCO-RLF: Hybrid classification optimization using recurrent learning and fuzzy for COVID-19 detection on CT images
K Balasamy, V Seethalakshmi - Biomedical Signal Processing and Control, 2025 - Elsevier
COVID-19 infection detection through initial lesion classification provides early diagnosis
and prevents breathing difficulties. Detecting the infectious part of the lungs using …
and prevents breathing difficulties. Detecting the infectious part of the lungs using …
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 …
WFT-Fati-Dec: Enhanced Fatigue Detection AI System Based on Wavelet Denoising and Fourier Transform
As the number of road accidents increases, it is critical to avoid making driving mistakes.
Driver fatigue detection is a concern that has prompted researchers to develop numerous …
Driver fatigue detection is a concern that has prompted researchers to develop numerous …
Improving Brain Tumor Classification: An Approach Integrating Pre-Trained CNN Models and Machine Learning Algorithms
Accurate detection of brain tumors is crucial for enhancing patient outcomes, yet the
interpretation of Magnetic Resonance Imaging (MRI) scans poses significant challenges …
interpretation of Magnetic Resonance Imaging (MRI) scans poses significant challenges …