Adel Elmaghraby:
AI-Powered Nailfold Capillary Analysis
Adel Elmaghraby, Mona Ebadi Jalal, Omar S. Emam, Cristián Castillo-Olea, and Begoña García-Zapirain introduce a deep learning model using EfficientNet-B0 with cascade transfer learning for detecting abnormalities in nailfold capillary images. Their model achieved perfect classification accuracy (1.00), significantly outperforming traditional methods. This AI-driven approach enhances early disease detection and improves diagnostic efficiency in conditions like diabetes and cardiovascular diseases. The study highlights the potential of AI in medical imaging and aims for further validation on larger datasets for clinical applications.
PSO-Based Biomedical Image Registration
Adel S. Elmaghraby, alongside Mark P. Wachowiak, Renata Smolíková, Yufeng Zheng, and Jacek M. Zurada, introduces a groundbreaking image registration method using Particle Swarm Optimization (PSO). This hybrid approach leverages user guidance and evolutionary strategies to enhance accuracy and efficiency in aligning 2D and 3D biomedical images. The technique significantly surpasses traditional methods, showcasing its potential for advancing diagnostics, treatment planning, and image-guided interventions.
Deep Learning for Speech Emotion Recognition
Adel Elmaghraby, Babak Joze Abbaschian, and Daniel Sierra-Sosa
This study explores deep learning techniques for speech emotion recognition, comparing CNNs, LSTMs, and GANs across multiple datasets. It discusses advancements, challenges, and future prospects in AI-driven emotion detection, enhancing human-computer interaction.