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Machine Learning and AI for Medical Data Analysis

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 44891

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Special Issue Editors

Prof. Dr. Sang-Woong Lee

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Guest Editor

School of Computing, Gachon University, Seongnam 13120, Republic of Korea
Interests: deep learning; computer vision; image processing; brain science; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Dr. O-Joun Lee

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Department of Artificial Intelligence, The Catholic University of Korea, Bucheon 14662, Republic of Korea
Interests: computational narrative; artificial intelligence; knowledge representation
Special Issues, Collections and Topics in MDPI journals

Dr. Muhammad Adnan Khan

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Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Raiwind Road Campus, Lahore 54000, Pakistan
Interests: machine learning; computational intelligence

Dr. Ngoc Dung Bui

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Guest Editor

Faculty of Information Technology, University of Transport and Communications, 3 Cau Giay, Dong Da, Hanoi, Vietnam
Interests: machine learning; complex network

Special Issue Information

Dear Colleagues,

The rapid development of artificial intelligence (AI), especially in machine learning (ML), has recently promoted the development of decision-making support systems for medical diagnostics. Convolutional neural network models have exhibited remarkable performance in recognizing, segmenting, and classifying diseased areas by analyzing biomedical images, and knowledge graph completion methods have been employed to predict medicinal effects and discover medical properties. However, quite a few existing studies applied AI techniques to medical data without significant modification. Despite their high accuracy, the unspecialized approaches have difficulties in dealing with characteristics of medical data, such as scarcity and imbalance.

Therefore, this Special Issue aims to gather recent research focusing on AI and ML methodologies specialized in medical data. Of particular interest are submissions regarding attempts to solve issues surrounding the scarcity and imbalance of medical data (including, but not limited to, data-efficient domain adaption, transfer learning, synthetic data generation, and data augmentation). However, contributions concerning image/signal/natural language processing in medical data are also welcomed.

Topics of Interest:

Machine learning and artificial intelligence for biomedical applications;
Biomedical image/signal analysis;
Biomedical image/signal processing;
Biomedical image reconstruction;
Sensing, detection and recognition in biomedical image/signals;
Medical data acquisition, cleaning and integration using AI methodologies;
Data-efficient domain adaptation and transfer learning;
Synthetic data generation and data augmentation for biomedical applications;
Natural language processing and knowledge discovery in medical documents;
Drug effect/side effect prediction using artificial intelligence or knowledge engineering;
Automated/computer-aided diagnosis using artificial intelligence or knowledge engineering.

Prof. Dr. Sang-Woong Lee
Dr. O-Joun Lee
Dr. Muhammad Adnan Khan
Dr. Ngoc Dung Bui
Guest Editors

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Keywords

biomedical image/signal processing
automated/computer-aided diagnosis
domain adaptation
transfer learning

Published Papers (13 papers)

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Research

16 pages, 9001 KiB

Open AccessArticle

MMNet: A Mixing Module Network for Polyp Segmentation

by Raman Ghimire and Sang-Woong Lee

Sensors 2023, 23(16), 7258; https://doi.org/10.3390/s23167258 - 18 Aug 2023

Viewed by 1037

Abstract

Traditional encoder–decoder networks like U-Net have been extensively used for polyp segmentation. However, such networks have demonstrated limitations in explicitly modeling long-range dependencies. In such networks, local patterns are emphasized over the global context, as each convolutional kernel focuses on only a local subset of pixels in the entire image. Several recent transformer-based networks have been shown to overcome such limitations. Such networks encode long-range dependencies using self-attention methods and thus learn highly expressive representations. However, due to the computational complexity of modeling the whole image, self-attention is expensive to compute, as there is a quadratic increment in cost with the increase in pixels in the image. Thus, patch embedding has been utilized, which groups small regions of the image into single input features. Nevertheless, these transformers still lack inductive bias, even with the image as a 1D sequence of visual tokens. This results in the inability to generalize to local contexts due to limited low-level features. We introduce a hybrid transformer combined with a convolutional mixing network to overcome computational and long-range dependency issues. A pretrained transformer network is introduced as a feature-extracting encoder, and a mixing module network (MMNet) is introduced to capture the long-range dependencies with a reduced computational cost. Precisely, in the mixing module network, we use depth-wise and 1 × 1 convolution to model long-range dependencies to establish spatial and cross-channel correlation, respectively. The proposed approach is evaluated qualitatively and quantitatively on five challenging polyp datasets across six metrics. Our MMNet outperforms the previous best polyp segmentation methods. Full article

(This article belongs to the Special Issue Machine Learning and AI for Medical Data Analysis)

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21 pages, 5232 KiB

Open AccessArticle

Bayesian Optimization with Support Vector Machine Model for Parkinson Disease Classification

by Ahmed M. Elshewey, Mahmoud Y. Shams, Nora El-Rashidy, Abdelghafar M. Elhady, Samaa M. Shohieb and Zahraa Tarek

Sensors 2023, 23(4), 2085; https://doi.org/10.3390/s23042085 - 13 Feb 2023

Cited by 24 | Viewed by 4866

Abstract

Parkinson’s disease (PD) has become widespread these days all over the world. PD affects the nervous system of the human and also affects a lot of human body parts that are connected via nerves. In order to make a classification for people who suffer from PD and who do not suffer from the disease, an advanced model called Bayesian Optimization-Support Vector Machine (BO-SVM) is presented in this paper for making the classification process. Bayesian Optimization (BO) is a hyperparameter tuning technique for optimizing the hyperparameters of machine learning models in order to obtain better accuracy. In this paper, BO is used to optimize the hyperparameters for six machine learning models, namely, Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), Ridge Classifier (RC), and Decision Tree (DT). The dataset used in this study consists of 23 features and 195 instances. The class label of the target feature is 1 and 0, where 1 refers to the person suffering from PD and 0 refers to the person who does not suffer from PD. Four evaluation metrics, namely, accuracy, F1-score, recall, and precision were computed to evaluate the performance of the classification models used in this paper. The performance of the six machine learning models was tested on the dataset before and after the process of hyperparameter tuning. The experimental results demonstrated that the SVM model achieved the best results when compared with other machine learning models before and after the process of hyperparameter tuning, with an accuracy of 92.3% obtained using BO. Full article

(This article belongs to the Special Issue Machine Learning and AI for Medical Data Analysis)

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18 pages, 14492 KiB

Open AccessArticle

Unsupervised Domain Adaptation with Shape Constraint and Triple Attention for Joint Optic Disc and Cup Segmentation

by Fengming Zhang, Shuiwang Li and Jianzhi Deng

Sensors 2022, 22(22), 8748; https://doi.org/10.3390/s22228748 - 12 Nov 2022

Viewed by 1270

Abstract

Currently, glaucoma has become an important cause of blindness. At present, although glaucoma cannot be cured, early treatment can prevent it from getting worse. A reliable way to detect glaucoma is to segment the optic disc and cup and then measure the cup-to-disc ratio (CDR). Many deep neural network models have been developed to autonomously segment the optic disc and the optic cup to help in diagnosis. However, their performance degrades when subjected to domain shift. While many domain-adaptation methods have been exploited to address this problem, they are apt to produce malformed segmentation results. In this study, it is suggested that the segmentation network be adjusted using a constrained formulation that embeds prior knowledge about the shape of the segmentation areas that is domain-invariant. Based on IOSUDA (i.e., Input and Output Space Unsupervised Domain Adaptation), a novel unsupervised joint optic cup-to-disc segmentation framework with shape constraints is proposed, called SCUDA (short for Shape-Constrained Unsupervised Domain Adaptation). A shape constrained loss function is novelly proposed in this paper which utilizes domain-invariant prior knowledge concerning the segmentation region of the joint optic cup–optical disc of fundus images to constrain the segmentation result during network training. In addition, a convolutional triple attention module is designed to improve the segmentation network, which captures cross-dimensional interactions and provides a rich feature representation to improve the segmentation accuracy. Experiments on the RIM-ONE_r3 and Drishti-GS datasets demonstrate that the algorithm outperforms existing approaches for segmenting optic discs and cups. Full article

(This article belongs to the Special Issue Machine Learning and AI for Medical Data Analysis)

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24 pages, 2385 KiB

Open AccessArticle

Skin Lesion Classification on Imbalanced Data Using Deep Learning with Soft Attention

by Viet Dung Nguyen, Ngoc Dung Bui and Hoang Khoi Do

Sensors 2022, 22(19), 7530; https://doi.org/10.3390/s22197530 - 04 Oct 2022

Cited by 7 | Viewed by 2731

Abstract

Today, the rapid development of industrial zones leads to an increased incidence of skin diseases because of polluted air. According to a report by the American Cancer Society, it is estimated that in 2022 there will be about 100,000 people suffering from skin cancer and more than 7600 of these people will not survive. In the context that doctors at provincial hospitals and health facilities are overloaded, doctors at lower levels lack experience, and having a tool to support doctors in the process of diagnosing skin diseases quickly and accurately is essential. Along with the strong development of artificial intelligence technologies, many solutions to support the diagnosis of skin diseases have been researched and developed. In this paper, a combination of one Deep Learning model (DenseNet, InceptionNet, ResNet, etc) with Soft-Attention, which unsupervisedly extract a heat map of main skin lesions. Furthermore, personal information including age and gender are also used. It is worth noting that a new loss function that takes into account the data imbalance is also proposed. Experimental results on data set HAM10000 show that using InceptionResNetV2 with Soft-Attention and the new loss function gives 90 percent accuracy, mean of precision, F1-score, recall, and AUC of 0.81, 0.81, 0.82, and 0.99, respectively. Besides, using MobileNetV3Large combined with Soft-Attention and the new loss function, even though the number of parameters is 11 times less and the number of hidden layers is 4 times less, it achieves an accuracy of 0.86 and 30 times faster diagnosis than InceptionResNetV2. Full article

(This article belongs to the Special Issue Machine Learning and AI for Medical Data Analysis)

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18 pages, 2616 KiB

Open AccessArticle

SCDNet: A Deep Learning-Based Framework for the Multiclassification of Skin Cancer Using Dermoscopy Images

by Ahmad Naeem, Tayyaba Anees, Makhmoor Fiza, Rizwan Ali Naqvi and Seung-Won Lee

Sensors 2022, 22(15), 5652; https://doi.org/10.3390/s22155652 - 28 Jul 2022

Cited by 29 | Viewed by 3074

Abstract

Skin cancer is a deadly disease, and its early diagnosis enhances the chances of survival. Deep learning algorithms for skin cancer detection have become popular in recent years. A novel framework based on deep learning is proposed in this study for the multiclassification of skin cancer types such as Melanoma, Melanocytic Nevi, Basal Cell Carcinoma and Benign Keratosis. The proposed model is named as SCDNet which combines Vgg16 with convolutional neural networks (CNN) for the classification of different types of skin cancer. Moreover, the accuracy of the proposed method is also compared with the four state-of-the-art pre-trained classifiers in the medical domain named Resnet 50, Inception v3, AlexNet and Vgg19. The performance of the proposed SCDNet classifier, as well as the four state-of-the-art classifiers, is evaluated using the ISIC 2019 dataset. The accuracy rate of the proposed SDCNet is 96.91% for the multiclassification of skin cancer whereas, the accuracy rates for Resnet 50, Alexnet, Vgg19 and Inception-v3 are 95.21%, 93.14%, 94.25% and 92.54%, respectively. The results showed that the proposed SCDNet performed better than the competing classifiers. Full article

(This article belongs to the Special Issue Machine Learning and AI for Medical Data Analysis)

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15 pages, 3082 KiB

Open AccessArticle

An Ensemble Approach to Predict Early-Stage Diabetes Risk Using Machine Learning: An Empirical Study

by Umm e Laila, Khalid Mahboob, Abdul Wahid Khan, Faheem Khan and Whangbo Taekeun

Sensors 2022, 22(14), 5247; https://doi.org/10.3390/s22145247 - 13 Jul 2022

Cited by 61 | Viewed by 4131

Abstract

Diabetes is a long-lasting disease triggered by expanded sugar levels in human blood and can affect various organs if left untreated. It contributes to heart disease, kidney issues, damaged nerves, damaged blood vessels, and blindness. Timely disease prediction can save precious lives and enable healthcare advisors to take care of the conditions. Most diabetic patients know little about the risk factors they face before diagnosis. Nowadays, hospitals deploy basic information systems, which generate vast amounts of data that cannot be converted into proper/useful information and cannot be used to support decision making for clinical purposes. There are different automated techniques available for the earlier prediction of disease. Ensemble learning is a data analysis technique that combines multiple techniques into a single optimal predictive system to evaluate bias and variation, and to improve predictions. Diabetes data, which included 17 variables, were gathered from the UCI repository of various datasets. The predictive models used in this study include AdaBoost, Bagging, and Random Forest, to compare the precision, recall, classification accuracy, and F1-score. Finally, the Random Forest Ensemble Method had the best accuracy (97%), whereas the AdaBoost and Bagging algorithms had lower accuracy, precision, recall, and F1-scores. Full article

(This article belongs to the Special Issue Machine Learning and AI for Medical Data Analysis)

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13 pages, 581 KiB

Open AccessArticle

Generation of Individualized Synthetic Data for Augmentation of the Type 1 Diabetes Data Sets Using Deep Learning Models

by Josep Noguer, Ivan Contreras, Omer Mujahid, Aleix Beneyto and Josep Vehi

Sensors 2022, 22(13), 4944; https://doi.org/10.3390/s22134944 - 30 Jun 2022

Cited by 9 | Viewed by 2651

Abstract

In this paper, we present a methodology based on generative adversarial network architecture to generate synthetic data sets with the intention of augmenting continuous glucose monitor data from individual patients. We use these synthetic data with the aim of improving the overall performance of prediction models based on machine learning techniques. Experiments were performed on two cohorts of patients suffering from type 1 diabetes mellitus with significant differences in their clinical outcomes. In the first contribution, we have demonstrated that the chosen methodology is able to replicate the intrinsic characteristics of individual patients following the statistical distributions of the original data. Next, a second contribution demonstrates the potential of synthetic data to improve the performance of machine learning approaches by testing and comparing different prediction models for the problem of predicting nocturnal hypoglycemic events in type 1 diabetic patients. The results obtained for both generative and predictive models are quite encouraging and set a precedent in the use of generative techniques to train new machine learning models. Full article

(This article belongs to the Special Issue Machine Learning and AI for Medical Data Analysis)

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20 pages, 830 KiB

Open AccessArticle

A Two-Step Approach for Classification in Alzheimer’s Disease

by Ivanoe De Falco, Giuseppe De Pietro and Giovanna Sannino

Sensors 2022, 22(11), 3966; https://doi.org/10.3390/s22113966 - 24 May 2022

Cited by 6 | Viewed by 1868

Abstract

The classification of images is of high importance in medicine. In this sense, Deep learning methodologies show excellent performance with regard to accuracy. The drawback of these methodologies is the fact that they are black boxes, so no explanation is given to users on the reasons underlying their choices. In the medical domain, this lack of transparency and information, typical of black box models, brings practitioners to raise concerns, and the result is a resistance to the use of deep learning tools. In order to overcome this problem, a different Machine Learning approach to image classification is used here that is based on interpretability concepts thanks to the use of an evolutionary algorithm. It relies on the application of two steps in succession. The first receives a set of images in the inut and performs image filtering on them so that a numerical data set is generated. The second is a classifier, the kernel of which is an evolutionary algorithm. This latter, at the same time, classifies and automatically extracts explicit knowledge as a set of IF–THEN rules. This method is investigated with respect to a data set of MRI brain imagery referring to Alzheimer’s disease. Namely, a two-class data set (non-demented and moderate demented) and a three-class data set (non-demented, mild demented, and moderate demented) are extracted. The methodology shows good results in terms of accuracy (100% for the best run over the two-class problem and 91.49% for the best run over the three-class one),

F_s c o r e

(1.0000 and 0.9149, respectively), and Matthews Correlation Coefficient (1.0000 and 0.8763, respectively). To ascertain the quality of these results, they are contrasted against those from a wide set of well-known classifiers. The outcome of this comparison is that, in both problems, the methodology achieves the best results in terms of accuracy and

F_s c o r e

, whereas, for the Matthews Correlation Coefficient, it has the best result over the two-class problem and the second over the three-class one. Full article

(This article belongs to the Special Issue Machine Learning and AI for Medical Data Analysis)

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19 pages, 10115 KiB

Open AccessArticle

ECG Data Analysis with Denoising Approach and Customized CNNs

by Abhinav Mishra, Ganapathiraju Dharahas, Shilpa Gite, Ketan Kotecha, Deepika Koundal, Atef Zaguia, Manjit Kaur and Heung-No Lee

Sensors 2022, 22(5), 1928; https://doi.org/10.3390/s22051928 - 01 Mar 2022

Cited by 14 | Viewed by 4452

Abstract

In the last decade, the proactive diagnosis of diseases with artificial intelligence and its aligned technologies has been an exciting and fruitful area. One of the areas in medical care where constant monitoring is required is cardiovascular diseases. Arrhythmia, one of the cardiovascular diseases, is generally diagnosed by doctors using Electrocardiography (ECG), which records the heart’s rhythm and electrical activity. The use of neural networks has been extensively adopted to identify abnormalities in the last few years. It is found that the probability of detecting arrhythmia increases if the denoised signal is used rather than the raw input signal. This paper compares six filters implemented on ECG signals to improve classification accuracy. Custom convolutional neural networks (CCNNs) are designed to filter ECG data. Extensive experiments are drawn by considering the six ECG filters and the proposed custom CCNN models. Comparative analysis reveals that the proposed models outperform the competitive models in various performance metrics. Full article

(This article belongs to the Special Issue Machine Learning and AI for Medical Data Analysis)

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37 pages, 5305 KiB

Open AccessArticle

Untangling Computer-Aided Diagnostic System for Screening Diabetic Retinopathy Based on Deep Learning Techniques

by Muhammad Shoaib Farooq, Ansif Arooj, Roobaea Alroobaea, Abdullah M. Baqasah, Mohamed Yaseen Jabarulla, Dilbag Singh and Ruhama Sardar

Sensors 2022, 22(5), 1803; https://doi.org/10.3390/s22051803 - 24 Feb 2022

Cited by 21 | Viewed by 4157

Abstract

Diabetic Retinopathy (DR) is a predominant cause of visual impairment and loss. Approximately 285 million worldwide population is affected with diabetes, and one-third of these patients have symptoms of DR. Specifically, it tends to affect the patients with 20 years or more with diabetes, but it can be reduced by early detection and proper treatment. Diagnosis of DR by using manual methods is a time-consuming and expensive task which involves trained ophthalmologists to observe and evaluate DR using digital fundus images of the retina. This study aims to systematically find and analyze high-quality research work for the diagnosis of DR using deep learning approaches. This research comprehends the DR grading, staging protocols and also presents the DR taxonomy. Furthermore, identifies, compares, and investigates the deep learning-based algorithms, techniques, and, methods for classifying DR stages. Various publicly available dataset used for deep learning have also been analyzed and dispensed for descriptive and empirical understanding for real-time DR applications. Our in-depth study shows that in the last few years there has been an increasing inclination towards deep learning approaches. 35% of the studies have used Convolutional Neural Networks (CNNs), 26% implemented the Ensemble CNN (ECNN) and, 13% Deep Neural Networks (DNN) are amongst the most used algorithms for the DR classification. Thus using the deep learning algorithms for DR diagnostics have future research potential for DR early detection and prevention based solution. Full article

(This article belongs to the Special Issue Machine Learning and AI for Medical Data Analysis)

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22 pages, 4882 KiB

Open AccessArticle

Automated Knee MR Images Segmentation of Anterior Cruciate Ligament Tears

by Mazhar Javed Awan, Mohd Shafry Mohd Rahim, Naomie Salim, Amjad Rehman and Begonya Garcia-Zapirain

Sensors 2022, 22(4), 1552; https://doi.org/10.3390/s22041552 - 17 Feb 2022

Cited by 24 | Viewed by 4032

Abstract

The anterior cruciate ligament (ACL) is one of the main stabilizer parts of the knee. ACL injury leads to causes of osteoarthritis risk. ACL rupture is common in the young athletic population. Accurate segmentation at an early stage can improve the analysis and classification of anterior cruciate ligaments tears. This study automatically segmented the anterior cruciate ligament (ACL) tears from magnetic resonance imaging through deep learning. The knee mask was generated on the original Magnetic Resonance (MR) images to apply a semantic segmentation technique with convolutional neural network architecture U-Net. The proposed segmentation method was measured by accuracy, intersection over union (IoU), dice similarity coefficient (DSC), precision, recall and F1-score of 98.4%, 99.0%, 99.4%, 99.6%, 99.6% and 99.6% on 11451 training images, whereas on the validation images of 3817 was, respectively, 97.7%, 93.8%,96.8%, 96.5%, 97.3% and 96.9%. We also provide dice loss of training and test datasets that have remained 0.005 and 0.031, respectively. The experimental results show that the ACL segmentation on JPEG MRI images with U-Nets achieves accuracy that outperforms the human segmentation. The strategy has promising potential applications in medical image analytics for the segmentation of knee ACL tears for MR images. Full article

(This article belongs to the Special Issue Machine Learning and AI for Medical Data Analysis)

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20 pages, 5255 KiB

Open AccessArticle

Mammography Image-Based Diagnosis of Breast Cancer Using Machine Learning: A Pilot Study

by Maha M. Alshammari, Afnan Almuhanna and Jamal Alhiyafi

Sensors 2022, 22(1), 203; https://doi.org/10.3390/s22010203 - 28 Dec 2021

Cited by 13 | Viewed by 3329

Abstract

A tumor is an abnormal tissue classified as either benign or malignant. A breast tumor is one of the most common tumors in women. Radiologists use mammograms to identify a breast tumor and classify it, which is a time-consuming process and prone to error due to the complexity of the tumor. In this study, we applied machine learning-based techniques to assist the radiologist in reading mammogram images and classifying the tumor in a very reasonable time interval. We extracted several features from the region of interest in the mammogram, which the radiologist manually annotated. These features are incorporated into a classification engine to train and build the proposed structure classification models. We used a dataset that was not previously seen in the model to evaluate the accuracy of the proposed system following the standard model evaluation schemes. Accordingly, this study found that various factors could affect the performance, which we avoided after experimenting all the possible ways. This study finally recommends using the optimized Support Vector Machine or Naïve Bayes, which produced 100% accuracy after integrating the feature selection and hyper-parameter optimization schemes. Full article

(This article belongs to the Special Issue Machine Learning and AI for Medical Data Analysis)

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19 pages, 4950 KiB

Open AccessArticle

An Efficient Methodology for Brain MRI Classification Based on DWT and Convolutional Neural Network

by Muhammad Fayaz, Nurlan Torokeldiev, Samat Turdumamatov, Muhammad Shuaib Qureshi, Muhammad Bilal Qureshi and Jeonghwan Gwak

Sensors 2021, 21(22), 7480; https://doi.org/10.3390/s21227480 - 10 Nov 2021

Cited by 12 | Viewed by 3284

Abstract

In this paper, a model based on discrete wavelet transform and convolutional neural network for brain MR image classification has been proposed. The proposed model is comprised of three main stages, namely preprocessing, feature extraction, and classification. In the preprocessing, the median filter has been applied to remove salt-and-pepper noise from the brain MRI images. In the discrete wavelet transform, discrete Harr wavelet transform has been used. In the proposed model, 3-level Harr wavelet decomposition has been applied on the images to remove low-level detail and reduce the size of the images. Next, the convolutional neural network has been used for classifying the brain MR images into normal and abnormal. The convolutional neural network is also a prevalent classification method and has been widely used in different areas. In this study, the convolutional neural network has been used for brain MRI classification. The proposed methodology has been applied to the standard dataset, and for performance evaluation, we have used different performance evaluation measures. The results indicate that the proposed method provides good results with 99% accuracy. The proposed method results are then presented for comparison with some state-of-the-art algorithms where simply the proposed method outperforms the counterpart algorithms. The proposed model has been developed to be used for practical applications. Full article

(This article belongs to the Special Issue Machine Learning and AI for Medical Data Analysis)

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