Popular deep learning algorithms for disease prediction: a review

Article Computer Science, Theory & Methods

A Prior-mask-guided Few-shot Learning for Skin Lesion Segmentation

Junsheng Xiao et al.

Summary: This study proposes a few-shot segmentation network for skin lesion segmentation, which allows for quick and accurate identification of skin lesions, thereby improving patient survival rate.

COMPUTING (2023)

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Article Gastroenterology & Hepatology

Use of Artificial Intelligence in the Prediction of Malignant Potential of Gastric Gastrointestinal Stromal Tumors

Gulseren Seven et al.

Summary: AI via a deep learning algorithm using EUS images can predict the malignant potential of gastric GISTs with high accuracy.

DIGESTIVE DISEASES AND SCIENCES (2022)

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Article Computer Science, Artificial Intelligence

Digital twins based on bidirectional LSTM and GAN for modelling the COVID-19 pandemic

Cesar Quilodran-Casas et al.

Summary: The outbreak of COVID-19 highlights the importance of studying epidemiological models, with recent advancements in machine learning techniques offering more efficient and accurate predictions at reduced computational costs.

NEUROCOMPUTING (2022)

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Article Computer Science, Artificial Intelligence

Enhancing Factorization Machines With Generalized Metric Learning

Yangyang Guo et al.

Summary: This paper proposes a Factorization Machines (FM) framework equipped with generalized metric learning techniques to better capture feature correlations. Specifically, a Mahalanobis distance and a deep neural network (DNN) method are introduced to effectively model linear and non-linear correlations, respectively. Experimental results demonstrate that our framework outperforms several state-of-the-art baselines on benchmark datasets.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2022)

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Article Computer Science, Software Engineering

A Vision for Leveraging the Concept of Digital Twins to Support the Provision of Personalized Cancer Care

Nilmini Wickramasinghe et al.

Summary: This article explores the opportunity of applying digital twins in healthcare for more personalized care. It provides a classification of digital twins into Grey Box, Surrogate, and Black Box models and explores the use of Black Box digital twins in personalized uterine cancer care. It represents a novel attempt that brings together the domains of clinical, digital health, and computer science.

IEEE INTERNET COMPUTING (2022)

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Article Computer Science, Information Systems

Entropy Slicing Extraction and Transfer Learning Classification for Early Diagnosis of Alzheimer Diseases with sMRI

S. Sambath Kumar et al.

Summary: A new model utilizing deep networks and sMRI data has been proposed for early diagnosis of Alzheimer's Disease, achieving high accuracy rates in classification tasks.

ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS (2021)

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Article Computer Science, Artificial Intelligence

Computer-aided detection of brain tumor from magnetic resonance images using deep learning network

Maibam Mangalleibi Chanu et al.

Summary: Brain tumor is a life-threatening disease caused by cell proliferation in the brain, and timely diagnosis and treatment are essential. This paper proposed a computer-aided approach using 2D convolutional neural network to classify brain MRI images with higher accuracy compared to other methods.

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING (2021)

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Article Computer Science, Artificial Intelligence

Neural attention model for recommendation based on factorization machines

Peng Wen et al.

Summary: In recommendation systems, it is crucial to consider various information aspects, mine and learn high-order feature interactions for accurate recommendations. A neural attention model (NAM) is proposed to deepen factorization machines (FMs) with attention mechanism and fully connected layers. Experimental results demonstrate the superior performance of NAM for prediction under sparse data.

APPLIED INTELLIGENCE (2021)

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Article Biophysics

Towards enabling a cardiovascular digital twin for human systemic circulation using inverse analysis

Neeraj Kavan Chakshu et al.

Summary: This paper proposes a method to create a cardiovascular digital twin using inverse analysis and recurrent neural networks to calculate blood pressure waveforms in the cardiovascular system. The approach shows potential in detecting abdominal aortic aneurysm and assessing its severity.

BIOMECHANICS AND MODELING IN MECHANOBIOLOGY (2021)

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Article Computer Science, Artificial Intelligence

MetaCOVID: A Siamese neural network framework with contrastive loss for n -shot diagnosis of COVID-19 patients

Mohammad Shorfuzzaman et al.

Summary: An AI system based on deep meta learning was proposed to accelerate analysis of chest X-ray (CXR) images for automatic detection of COVID-19 cases. The model integrates contrastive learning and a fine-tuned pre-trained ConvNet encoder to capture unbiased feature representations, achieving 95.6% accuracy and AUC of 0.97 in diagnosing COVID-19 from CXR images.

PATTERN RECOGNITION (2021)

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Article Computer Science, Artificial Intelligence

Toward better prediction of recurrence for Cushing's disease: a factorization-machine based neural approach

Yanghua Fan et al.

Summary: Cushing's disease is rare but recurrence prediction is crucial for individual treatment strategies. The use of a factorization machine-based neural network model can effectively predict recurrence after TTS surgery for CD patients.

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS (2021)

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Review Computer Science, Interdisciplinary Applications

A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues

Shahab Shamshirband et al.

Summary: This paper investigates the application of deep learning approaches in healthcare systems, focusing on advanced network architectures, applications, and industry trends. The goal is to provide in-depth insights into the application of deep learning models in healthcare solutions, bridging deep learning techniques and human healthcare interpretability, and presenting existing challenges and future directions.

JOURNAL OF BIOMEDICAL INFORMATICS (2021)

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Article Computer Science, Interdisciplinary Applications

Tumor type detection in brain MR images of the deep model developed using hypercolumn technique, attention modules, and residual blocks

Mesut Togacar et al.

Summary: In this study, a deep learning model called BrainMRNet was used to detect masses in open-source brain MR images, with classification accuracy rates tested for different tumor types causing brain cancer. A new segmentation method was proposed, showing promising results in clinical settings for supporting medical decisions related to brain tumor detection.

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING (2021)

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Article Construction & Building Technology

Towards the sustainable development of smart cities through mass video surveillance: A response to the COVID-19 pandemic

Mohammad Shorfuzzaman et al.

Summary: Sustainable smart city initiatives worldwide have impacted citizens' lives and brought changes to society, showcasing the potential of data-driven smart applications. However, the COVID-19 pandemic has exposed the limitations of existing smart city deployments. This paper proposes a data-driven deep learning framework for sustainable smart city development, focusing on timely responses through mass video surveillance to combat the pandemic.

SUSTAINABLE CITIES AND SOCIETY (2021)

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Review Computer Science, Interdisciplinary Applications

A review of smart sensors coupled with Internet of Things and Artificial Intelligence approach for heart failure monitoring

Muni Raj Maurya et al.

Summary: There has been a growing demand for healthcare technologies in the past decade, particularly for early diagnosis and effective treatment of heart failure using advanced medical technology and machine learning algorithms. The challenges for the next decade include utilizing a growing repertoire of interconnected sensors and AI software to process large datasets.

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING (2021)

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Article Engineering, Electrical & Electronic

EEG-Based Pathology Detection for Home Health Monitoring

Ghulam Muhammad et al.

Summary: A remote pathology detection system based on EEG with a deep convolutional network achieved over 89% accuracy on a publicly available EEG signal database. The system was also evaluated in a cloud-based framework and showed comparable performance to using a local server.

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS (2021)

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Article Chemistry, Multidisciplinary

Gate Attentional Factorization Machines: An Efficient Neural Network Considering Both Accuracy and Speed

Huaidong Yu et al.

Summary: With the increasing complexity of recommendation system models, balancing accuracy and speed has become an urgent issue. While deep neural networks improve accuracy, the Gate Attention Factorization Machine model excels in both speed and accuracy.

APPLIED SCIENCES-BASEL (2021)

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Article Computer Science, Information Systems

Deep Field-Aware Interaction Machine for Click-Through Rate Prediction

Gaofeng Qi et al.

Summary: Researchers have proposed a novel neural CTR model named DeepFIM, which addresses the problem of short feature-length in a field by introducing Field-aware Interaction Machine (FIM), achieving comparable and even better results than existing methods.

MOBILE INFORMATION SYSTEMS (2021)

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Article Computer Science, Information Systems

A comparison of machine learning algorithms for diabetes prediction

Jobeda Jamal Khanam et al.

Summary: The study utilized data mining, machine learning algorithms, and neural network methods to predict diabetes, using the Pima Indian Diabetes dataset. It was found that the logistic regression and support vector machine models performed well in diabetes prediction, and the neural network model with two hidden layers provided 88.6% accuracy.

ICT EXPRESS (2021)

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Article Chemistry, Analytical

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Parvathaneni Naga Srinivasu et al.

Summary: Deep learning models, specifically MobileNet V2 and LSTM, have been proposed for efficient classification of skin diseases, showing higher accuracy and effectiveness compared to other state-of-the-art models. The use of a grey-level co-occurrence matrix helps assess diseased growth progression, with the proposed method outperforming others with over 85% accuracy.

SENSORS (2021)

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Article Computer Science, Artificial Intelligence

Three-class brain tumor classification using deep dense inception residual network

Srinath Kokkalla et al.

Summary: A deep dense inception residual network was proposed for three-class brain tumor classification, achieving high accuracy on a publicly available brain tumor image dataset. The customization of the output layer of Inception ResNet v2 with a deep dense network improved the classification accuracy, outperforming existing models with a mean accuracy of 99.69%.

SOFT COMPUTING (2021)

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Article Computer Science, Information Systems

Adversarial Examples-Security Threats to COVID-19 Deep Learning Systems in Medical IoT Devices

Md Abdur Rahman et al.

Summary: Medical IoT devices are being integrated into pandemic management systems like COVID-19, with researchers using deep learning algorithms to identify COVID-19 phenomena. However, these DL algorithms have security vulnerabilities to adversarial perturbations, making DL models vulnerable to attacks without defensive mechanisms.

IEEE INTERNET OF THINGS JOURNAL (2021)

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Article Computer Science, Information Systems

Automatic Detection of Congestive Heart Failure Based on a Hybrid Deep Learning Algorithm in the Internet of Medical Things

Wenlong Ning et al.

Summary: This study introduced an automatic CHF detection model based on a hybrid deep learning algorithm, which accurately classifies CHF signals and serves as an effective auxiliary tool in clinical detection of CHF patients.

IEEE INTERNET OF THINGS JOURNAL (2021)

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Article Medicine, General & Internal

Deep Learning for Classification of Bone Lesions on Routine MRI

Feyisope R. Eweje et al.

Summary: A deep learning algorithm was developed in this study to differentiate benign and malignant bone lesions using MRI images and patient demographics, achieving performance comparable to radiology experts. These findings could aid in the development of computer-aided diagnostic tools to reduce unnecessary referrals to specialized centers and biopsies.

EBIOMEDICINE (2021)

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Article Computer Science, Artificial Intelligence

Artificial Intelligence for Classification of Soft-Tissue Masses at US

Benjamin Wang et al.

Summary: The study aimed to train CNN models to classify benign and malignant soft-tissue masses on US images and differentiate three common benign masses. The trained CNN model demonstrated excellent performance in distinguishing malignant and benign masses, matching that of experienced musculoskeletal radiologists.

RADIOLOGY-ARTIFICIAL INTELLIGENCE (2021)

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Proceedings Paper Computer Science, Artificial Intelligence

Pigmented Skin Lesions Image Classification Based on Residual Network

Xiangyang Yin

Summary: The study demonstrates the practical significance of using convolutional neural networks for skin lesions image classification. By comparing two 50-layer ResNets based on different building blocks, it is found that using the bottleneck building block can improve model training speed while maintaining accuracy.

PROCEEDINGS OF 2021 6TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING TECHNOLOGIES (ICMLT 2021) (2021)

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Proceedings Paper Materials Science, Multidisciplinary

Early detection of Alzheimer disease using Gadolinium material

S. Soundarya et al.

Summary: Alzheimer's disease is a neurodegenerative disorder that primarily affects older adults, with brain imaging used for detection and deep learning algorithms considered as a better solution for accurate early identification.

MATERIALS TODAY-PROCEEDINGS (2021)

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Review Computer Science, Information Systems

The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities

M. Mazhar Rathore et al.

Summary: Digital twinning has emerged as a top technology trend in recent years, especially in the industrial sector, due to its integration with big data analytics and AI-ML techniques. Despite the lack of a systematic review on digital twinning focusing on the role of big data and AI-ML, this technology shows great potential for future developments in academia and industry. Through a systematic review, researchers have highlighted the importance of big data and AI-ML in the creation of digital twins for various industrial applications.

IEEE ACCESS (2021)

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Review Medical Informatics

MRI brain tumor medical images analysis using deep learning techniques: a systematic review

Sabaa Ahmed Yahya Al-Galal et al.

Summary: The rapid advancement of medical imaging technology poses challenges for medical experts and radiologists, necessitating automated analysis and classification techniques, with deep learning playing a key role in medical image analysis.

HEALTH AND TECHNOLOGY (2021)

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Article Computer Science, Information Systems

HoAFM: A High-order Attentive Factorization Machine for CTR Prediction

Zhulin Tao et al.

INFORMATION PROCESSING & MANAGEMENT (2020)

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Article Engineering, Electrical & Electronic

A Deep Learning-Based Framework for Automatic Brain Tumors Classification Using Transfer Learning

Arshia Rehman et al.

CIRCUITS SYSTEMS AND SIGNAL PROCESSING (2020)

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Brain tumor detection: a long short-term memory (LSTM)-based learning model

Javaria Amin et al.

NEURAL COMPUTING & APPLICATIONS (2020)

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Cell Nuclei Classification in Histopathological Images using Hybrid OL ConvNet

Suvidha Tripathi et al.

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The evidence landscape in precision medicine

Spencer Phillips Hey et al.

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Deep convolutional neural networks with transfer learning for automated brain image classification

Taranjit Kaur et al.

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Current Strategies and Future Perspectives for Precision Medicine in Pancreatic Cancer

Ivonne Regel et al.

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Generalizing from a Few Examples: A Survey on Few-shot Learning

Yaqing Wang et al.

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Explainable AI and Mass Surveillance System-Based Healthcare Framework to Combat COVID-I9 Like Pandemics

M. Shamim Hossain et al.

IEEE NETWORK (2020)

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Enhanced factorization machine via neural pairwise ranking and attention networks

Yonghong Yu et al.

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Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine

Ryuji Hamamoto et al.

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Deep Learning Based Pathology Detection for Smart Connected Healthcares

M. Shamim Hossain et al.

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Survey on RNN and CRF models for de-identification of medical free text

Joffrey L. Leevy et al.

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Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine

Zeeshan Ahmed et al.

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Precision Medicine Informatics: Principles, Prospects, and Challenges

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IEEE ACCESS (2020)

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Smart healthcare monitoring: a voice pathology detection paradigm for smart cities

M. Shamim Hossain et al.

MULTIMEDIA SYSTEMS (2019)

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Molecular profiling in precision medicine oncology

Christophe Le Tourneau et al.

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Causability and explainability of artificial intelligence in medicine

Andreas Holzinger et al.

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Radiogenomics for Precision Medicine With a Big Data Analytics Perspective

Andreas S. Panayides et al.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2019)

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A New Approach for Brain Tumor Segmentation and Classification Based on Score Level Fusion Using Transfer Learning

Javeria Amin et al.

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EMD2FNN: A strategy combining empirical mode decomposition and factorization machine based neural network for stock market trend prediction

Feng Zhou et al.

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Applying Deep Learning for Epilepsy Seizure Detection and Brain Mapping Visualization

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Development and Validation of a Deep Learning Algorithm for Mortality Prediction in Selecting Patients With Dementia for Earlier Palliative Care Interventions

Liqin Wang et al.

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High-Order Factorization Machine Based on Cross Weights Network for Recommendation

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Deep Learning-Based Parkinson's Disease Classification Using Vocal Feature Sets

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'Do-It-Yourself' Healthcare? Quality of Health and Healthcare Through Wearable Sensors

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Emotion-Aware Connected Healthcare Big Data Towards 5G

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A novel three-tier Internet of Things architecture with machine learning algorithm for early detection of heart diseases

Priyan Malarvizhi Kumar et al.

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Predicting hospital readmission for lupus patients: An RNN-LSTM-based deep-learning methodology

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Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals

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Popular deep learning algorithms for disease prediction: a review

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