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Technologies
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Machine Learning Methods for Bio-Medical Image and Signal Processing: Recent...
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Machine Learning Methods for Bio-Medical Image and Signal Processing: Recent Advances

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Information and Communication Technologies".

Deadline for manuscript submissions: closed (15 November 2023) | Viewed by 8425

Special Issue Editor

Dr. Lipo Wang

E-Mail Website
Guest Editor
School of Electrical and Electronic Engineering Nanyang Technological University Block S1, Nanyang Avenue, Singapore 639798, Singapore
Interests: machine learning; data mining; optimization; computational intelligence
Special Issues, Collections and Topics in MDPI journals

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Technologies is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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 (4 papers)

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Research

19 pages, 2787 KiB  
Article
Tongue Disease Prediction Based on Machine Learning Algorithms
by Ali Raad Hassoon, Ali Al-Naji, Ghaidaa A. Khalid and Javaan Chahl
Technologies 2024, 12(7), 97; https://doi.org/10.3390/technologies12070097 - 28 Jun 2024
Viewed by 369
Abstract
The diagnosis of tongue disease is based on the observation of various tongue characteristics, including color, shape, texture, and moisture, which indicate the patient’s health status. Tongue color is one such characteristic that plays a vital function in identifying diseases and the levels [...] Read more.
The diagnosis of tongue disease is based on the observation of various tongue characteristics, including color, shape, texture, and moisture, which indicate the patient’s health status. Tongue color is one such characteristic that plays a vital function in identifying diseases and the levels of progression of the ailment. With the development of computer vision systems, especially in the field of artificial intelligence, there has been important progress in acquiring, processing, and classifying tongue images. This study proposes a new imaging system to analyze and extract tongue color features at different color saturations and under different light conditions from five color space models (RGB, YcbCr, HSV, LAB, and YIQ). The proposed imaging system trained 5260 images classified with seven classes (red, yellow, green, blue, gray, white, and pink) using six machine learning algorithms, namely, the naïve Bayes (NB), support vector machine (SVM), k-nearest neighbors (KNN), decision trees (DTs), random forest (RF), and Extreme Gradient Boost (XGBoost) methods, to predict tongue color under any lighting conditions. The obtained results from the machine learning algorithms illustrated that XGBoost had the highest accuracy at 98.71%, while the NB algorithm had the lowest accuracy, with 91.43%. Based on these obtained results, the XGBoost algorithm was chosen as the classifier of the proposed imaging system and linked with a graphical user interface to predict tongue color and its related diseases in real time. Thus, this proposed imaging system opens the door for expanded tongue diagnosis within future point-of-care health systems. Full article
(This article belongs to the Special Issue Machine Learning Methods for Bio-Medical Image and Signal Processing: Recent Advances)
19 pages, 3509 KiB  
Article
Attention-Based Ensemble Network for Effective Breast Cancer Classification over Benchmarks
by Su Myat Thwin, Sharaf J. Malebary, Anas W. Abulfaraj and Hyun-Seok Park
Technologies 2024, 12(2), 16; https://doi.org/10.3390/technologies12020016 - 23 Jan 2024
Cited by 3 | Viewed by 2247
Abstract
Globally, breast cancer (BC) is considered a major cause of death among women. Therefore, researchers have used various machine and deep learning-based methods for its early and accurate detection using X-ray, MRI, and mammography image modalities. However, the machine learning model requires domain [...] Read more.
Globally, breast cancer (BC) is considered a major cause of death among women. Therefore, researchers have used various machine and deep learning-based methods for its early and accurate detection using X-ray, MRI, and mammography image modalities. However, the machine learning model requires domain experts to select an optimal feature, obtains a limited accuracy, and has a high false positive rate due to handcrafting features extraction. The deep learning model overcomes these limitations, but these models require large amounts of training data and computation resources, and further improvement in the model performance is needed. To do this, we employ a novel framework called the Ensemble-based Channel and Spatial Attention Network (ECS-A-Net) to automatically classify infected regions within BC images. The proposed framework consists of two phases: in the first phase, we apply different augmentation techniques to enhance the size of the input data, while the second phase includes an ensemble technique that parallelly leverages modified SE-ResNet50 and InceptionV3 as a backbone for feature extraction, followed by Channel Attention (CA) and Spatial Attention (SA) modules in a series manner for more dominant feature selection. To further validate the ECS-A-Net, we conducted extensive experiments between several competitive state-of-the-art (SOTA) techniques over two benchmarks, including DDSM and MIAS, where the proposed model achieved 96.50% accuracy for the DDSM and 95.33% accuracy for the MIAS datasets. Additionally, the experimental results demonstrated that our network achieved a better performance using various evaluation indicators, including accuracy, sensitivity, and specificity among other methods. 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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25 pages, 3593 KiB  
Article
A Novel Machine Learning-Based Prediction Method for Early Detection and Diagnosis of Congenital Heart Disease Using ECG Signal Processing
by Prabu Pachiyannan, Musleh Alsulami, Deafallah Alsadie, Abdul Khader Jilani Saudagar, Mohammed AlKhathami and Ramesh Chandra Poonia
Technologies 2024, 12(1), 4; https://doi.org/10.3390/technologies12010004 - 2 Jan 2024
Viewed by 3019
Abstract
Congenital heart disease (CHD) represents a multifaceted medical condition that requires early detection and diagnosis for effective management, given its diverse presentations and subtle symptoms that manifest from birth. This research article introduces a groundbreaking healthcare application, the Machine Learning-based Congenital Heart Disease [...] Read more.
Congenital heart disease (CHD) represents a multifaceted medical condition that requires early detection and diagnosis for effective management, given its diverse presentations and subtle symptoms that manifest from birth. This research article introduces a groundbreaking healthcare application, the Machine Learning-based Congenital Heart Disease Prediction Method (ML-CHDPM), tailored to address these challenges and expedite the timely identification and classification of CHD in pregnant women. The ML-CHDPM model leverages state-of-the-art machine learning techniques to categorize CHD cases, taking into account pertinent clinical and demographic factors. Trained on a comprehensive dataset, the model captures intricate patterns and relationships, resulting in precise predictions and classifications. The evaluation of the model’s performance encompasses sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve. Remarkably, the findings underscore the ML-CHDPM’s superiority across six pivotal metrics: accuracy, precision, recall, specificity, false positive rate (FPR), and false negative rate (FNR). The method achieves an average accuracy rate of 94.28%, precision of 87.54%, recall rate of 96.25%, specificity rate of 91.74%, FPR of 8.26%, and FNR of 3.75%. These outcomes distinctly demonstrate the ML-CHDPM’s effectiveness in reliably predicting and classifying CHD cases. This research marks a significant stride toward early detection and diagnosis, harnessing advanced machine learning techniques within the realm of ECG signal processing, specifically tailored to pregnant women. 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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26 pages, 6376 KiB  
Article
Optimal Integration of Machine Learning for Distinct Classification and Activity State Determination in Multiple Sclerosis and Neuromyelitis Optica
by Maha Gharaibeh, Wlla Abedalaziz, Noor Aldeen Alawad, Hasan Gharaibeh, Ahmad Nasayreh, Mwaffaq El-Heis, Maryam Altalhi, Agostino Forestiero and Laith Abualigah
Technologies 2023, 11(5), 131; https://doi.org/10.3390/technologies11050131 - 20 Sep 2023
Cited by 1 | Viewed by 2098
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 [...] Read more.
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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