Guest Editors
Dr. Tallha Akram, COMSATS University Islamabad, Pakistan.
Dr. Muhammad Attique Khan, HITEC University Taxila, Pakistan.
Summary
Machine learning based approaches are gaining a lot of attention due to the wide range of application in various fields. The last two decades witnessed the increasing interests in computer-aided medical system for early detection, diagnosis, prognosis, risk assessment and final therapy of diseases. The development of a reliable medical solution is a crucial task, because there is no single standard approach – covering all the subdomains including data processing, regions of interest detection, image segmentation and registration, image fusion and classification with high accuracy. Therefore, computer-aided diagnosis system is still a highly challenging domain which provides enough space for improvement. These days, deep learning-based methods are gaining much attention of researchers in machine learning community due to improved segmentation and classification results. Moreover, deep learning-based methods have also lowered the barriers of data preprocessing and extreme set of users’ dependability. Consequently, the processing burden in medical imaging is now shifted from human-side to computer-side. Thus, allowing more researchers to step into this well-liked and momentous area. This leads to improved performance, both in terms of accuracy and decision time.
This special issue seeks high-quality research articles generally dealing with the methods like semantic segmentation and deep learning in the field of medical image processing. We are only targeting original research articles, proposing novel solutions, covering new theories, and new implementations for medical image analytics.
Keywords
• Deep learning based Segmentation
• Federated Learning
• Semantic segmentation for Medical Infection Diagnosis
• Wireless capsule Endoscopy (WCE)imaging technology using deep learning
• Magnetic resonance imaging (MRI)
• Semantic techniques for MRI images
• FPGA with deep learning for medical imaging
• Mammogram Imaging Modality using deep learning
• Ultrasound Imaging Modality detection using Deep Learning
• X-ray computed tomography (CT)
• Deep learning based CAD systems
• Transfer learning in deep learning for medical imaging
• Cancers classification using deep learning
• Autoencoder based features selection using Deep Learning in Medical
• Fusion of convolutional layers in deep learning for recognition
• Optimal deep learning features selection for recognition
• Fusion of Image Modality using Deep Learning
• Deep Learning based Medical Imaging Retrieval
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