Special Issue Information

Dear Colleagues,

In the past few years, there has been tremendous success of machine learning methods, especially convolutional neural networks (CNN) and adversarial generative networks (GAN), applied to bio-medical image and signal processing tasks, such as classification, localization, detection, segmentation, and registration. This Special Issue aims at showcasing the recent advances in this exciting field and identifying research obstacles, emerging trends, and possible future directions.

Prof. Dr. Lipo Wang
Guest Editor

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Keywords

image enhancement & noise filtering
image restoration
feature extraction
interpolation & super-resolution
geometric transformations
image segmentation
motion analysis & tracking
computer and machine vision
imaging techniques & 3D imaging
remote sensing
forensics & security
filter design & digital filters
adaptive signal processing
spectral analysis
time-frequency signal analysis
speech & language processing
acoustics
multi-dimensional signal processing
biomedical imaging, image processing, signal processing and analysis
biomedical instrumentation, devices, sensors, artificial organs, and nano technologies
biomedical robotics and mechanics
wearable and real-time health monitoring systems
applications of artificial intelligence, machine learning and data mining in bioinformatics and medical informatics
healthcare information systems

Published Papers (1 paper)

Research

Open AccessArticle

Optimal Integration of Machine Learning for Distinct Classification and Activity State Determination in Multiple Sclerosis and Neuromyelitis Optica

and

Technologies 2023, 11(5), 131; https://doi.org/10.3390/technologies11050131 - 20 Sep 2023

Viewed by 307

Abstract

The intricate neuroinflammatory diseases multiple sclerosis (MS) and neuromyelitis optica (NMO) often present similar clinical symptoms, creating challenges in their precise detection via magnetic resonance imaging (MRI). This challenge is further compounded when detecting the active and inactive states of MS. To address this diagnostic problem, we introduce an innovative framework that incorporates state-of-the-art machine learning algorithms applied to features culled from MRI scans by pre-trained deep learning models, VGG-NET and InceptionV3. To develop and test this methodology, we utilized a robust dataset obtained from the King Abdullah University Hospital in Jordan, encompassing cases diagnosed with both MS and NMO. We benchmarked thirteen distinct machine learning algorithms and discovered that support vector machine (SVM) and K-nearest neighbor (KNN) algorithms performed superiorly in our context. Our results demonstrated KNN’s exceptional performance in differentiating between MS and NMO, with precision, recall, F1-score, and accuracy values of 0.98, 0.99, 0.99, and 0.99, respectively, using leveraging features extracted from VGG16. In contrast, SVM excelled in classifying active versus inactive states of MS, achieving precision, recall, F1-score, and accuracy values of 0.99, 0.97, 0.98, and 0.98, respectively, using leveraging features extracted from VGG16 and VGG19. Our advanced methodology outshines previous studies, providing clinicians with a highly accurate, efficient tool for diagnosing these diseases. The immediate implication of our research is the potential to streamline treatment processes, thereby delivering timely, appropriate care to patients suffering from these complex diseases. Full article

(This article belongs to the Special Issue Machine Learning Methods for Bio-Medical Image and Signal Processing: Recent Advances)

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