Brainnet: precision brain tumor classification with optimized efficientnet architecture
Brain tumors significantly impact human health due to their complexity and the challenges in
early detection and treatment. Accurate diagnosis is crucial for effective intervention, but …
early detection and treatment. Accurate diagnosis is crucial for effective intervention, but …
[HTML][HTML] Empowering covid-19 detection: Optimizing performance through fine-tuned efficientnet deep learning architecture
The worldwide COVID-19 pandemic has profoundly influenced the health and everyday
experiences of individuals across the planet. It is a highly contagious respiratory disease …
experiences of individuals across the planet. It is a highly contagious respiratory disease …
Machine and deep learning approaches for alzheimer disease detection using magnetic resonance images: An updated review
The most frequent chronic illness affecting the elderly and one with a high incidence rate is
Alzheimer's disease (AD). Deep Learning (DL) and Machine Learning (ML) techniques has …
Alzheimer's disease (AD). Deep Learning (DL) and Machine Learning (ML) techniques has …
TumorGANet: A transfer learning and generative adversarial network-based data augmentation model for brain tumor classification
Diagnosing brain tumors using magnetic resonance imaging (MRI) presents significant
challenges due to the complexities of segmentation and the variability in tumor …
challenges due to the complexities of segmentation and the variability in tumor …
A hybrid cardiovascular arrhythmia disease detection using ConvNeXt-X models on electrocardiogram signals
Cardiovascular arrhythmia, characterized by irregular heart rhythms, poses significant health
risks, including stroke and heart failure, making accurate and early detection critical for …
risks, including stroke and heart failure, making accurate and early detection critical for …
Utilizing deep feature Fusion for automatic leukemia classification: an Internet of Medical Things-Enabled deep learning framework
Acute lymphoblastic leukemia, commonly referred to as ALL, is a type of cancer that can
affect both the blood and the bone marrow. The process of diagnosis is a difficult one since it …
affect both the blood and the bone marrow. The process of diagnosis is a difficult one since it …
[HTML][HTML] Advancing Alzheimer's Disease Modelling by Develo** a Refined Biomimetic Brain Microenvironment for Facilitating High-Throughput Screening of …
Alzheimer's disease (AD) poses a significant worldwide health challenge, requiring novel
approaches for improved models and treatment development. This comprehensive review …
approaches for improved models and treatment development. This comprehensive review …
A hybrid multimodal machine learning model for Detecting Alzheimer's disease
J Sheng, Q Zhang, Q Zhang, L Wang, Z Yang… - Computers in Biology …, 2024 - Elsevier
Alzheimer's disease (AD) diagnosis utilizing single modality neuroimaging data has
limitations. Multimodal fusion of complementary biomarkers may improve diagnostic …
limitations. Multimodal fusion of complementary biomarkers may improve diagnostic …
Hybridized convolutional neural networks and long short-term memory for improved Alzheimer's disease diagnosis from MRI scans
Brain-related diseases are more sensitive than other diseases due to several factors,
including the complexity of surgical procedures, high costs, and other challenges …
including the complexity of surgical procedures, high costs, and other challenges …
Cancer Classification Utilizing Voting Classifier with Ensemble Feature Selection Method and Transcriptomic Data
Biomarker-based cancer identification and classification tools are widely used in
bioinformatics and machine learning fields. However, the high dimensionality of microarray …
bioinformatics and machine learning fields. However, the high dimensionality of microarray …