Machine Learning Applications in Image and Signal Processing: Challenges and Solutions
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: closed (15 April 2025) | Viewed by 558
Special Issue Editor
Interests: modelling and simulation; manufacturing; applied AI; socio-industrial studies
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The integration of machine learning (ML) with image and signal processing has revolutionized various fields by enhancing researchers’ ability to analyse and interpret complex data. ML algorithms can extract meaningful features from signals and images, improve classification accuracy, and enable advanced functionalities such as real-time processing and automated decision-making. This synergy between ML and image/signal processing has broad applications, including medical diagnostics, remote sensing, security, and multimedia.
This Special Issue highlights innovative research and addresses ongoing challenges in this field, with a particular focus on signal and image processing, signal reconstruction, image quantization, and advanced medical imaging applications. It serves as a platform for researchers to share their cutting-edge research, insights, and experiences, fostering collaboration and advancing the field.
This Special Issue’s scope includes a wide range of topics related to the machine learning applications in image and signal processing. Topics of interest include, but are not limited to, the following:
- Pre-trained models for imaging diagnosis;
- Image segmentation, quantization, classification and dimensionality reduction;
- Pattern recognition;
- Molecular imaging and nuclear medicine;
- X-ray, CT-scan, MRI, NMR, FMRI, and ultrasonography analysis;
- Machine learning applications in OCT, OCTA, and other ophthalmology imaging
- Biometric recognition systems;
- Integration of machine learning in imaging devices, imaging sequences, and imaging-guided intervention;
- Anomaly detection, signal denoising, and signal classification;
- Signal filtering and reconstruction.
Dr. Siamak Pedrammehr
Guest Editor
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Keywords
- machine learning
- supervised
- semi-supervised and unsupervised learning
- deep learning
- transfer learning
- ensemble learning applications
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