Identifying Severity Grading of Knee Osteoarthritis from X-ray Images Using an Efficient Mixture of Deep Learning and Machine Learning Models

Diagnostics, Год журнала: 2022, Номер 12(12), С. 2939 - 2939

Опубликована: Ноя. 24, 2022

Recently, many diseases have negatively impacted people's lifestyles. Among these, knee osteoarthritis (OA) has been regarded as the primary cause of activity restriction and impairment, particularly in older people. Therefore, quick, accurate, low-cost computer-based tools for early prediction OA patients are urgently needed. In this paper, part addressing issue, we developed a new method to efficiently diagnose classify severity based on X-ray images (i.e., binary multiclass) order study impact different class-based, which not yet addressed previous studies. This will provide physicians with variety deployment options future. Our proposed models basically divided into two frameworks applying pre-trained convolutional neural networks (CNN) feature extraction well fine-tuning CNN using transfer learning (TL) method. addition, traditional machine (ML) classifier is used exploit enriched space achieve better classification performance. first one, five classes-based extraction, principal component analysis (PCA) dimensionality reduction, support vector (SVM) classification. While second framework, few changes were made steps concept TL was fine-tune from framework fit classes, three four models. The evaluated data, their performance compared existing state-of-the-art It observed through conducted experimental demonstrate efficacy approach improving accuracy both multiclass class-based case study. Nonetheless, empirical results revealed that fewer labels used, achieved, class outperforming all, reached 90.8% rate. Furthermore, demonstrated contribution stage disease help reduce its progression improve quality life.

Язык: Английский

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Multi-Scale CNN: An Explainable AI-Integrated Unique Deep Learning Framework for Lung-Affected Disease Classification

Technologies, Год журнала: 2023, Номер 11(5), С. 134 - 134

Опубликована: Сен. 30, 2023

Lung-related diseases continue to be a leading cause of global mortality. Timely and precise diagnosis is crucial save lives, but the availability testing equipment remains challenge, often coupled with issues reliability. Recent research has highlighted potential Chest X-ray (CXR) images in identifying various lung diseases, including COVID-19, fibrosis, pneumonia, more. In this comprehensive study, four publicly accessible datasets have been combined create robust dataset comprising 6650 CXR images, categorized into seven distinct disease groups. To effectively distinguish between normal six different lung-related (namely, bacterial opacity, tuberculosis, viral pneumonia), Deep Learning (DL) architecture called Multi-Scale Convolutional Neural Network (MS-CNN) introduced. The model adapted classify multiple numbers classes, which considered persistent challenge field. While prior studies demonstrated high accuracy binary limited-class scenarios, proposed framework maintains across diverse range conditions. innovative harnesses power combining predictions from feature maps at resolution scales, significantly enhancing classification accuracy. approach aims shorten duration compared state-of-the-art models, offering solution toward expediting medical interventions for patients integrating explainable AI (XAI) prediction capability. results an impressive 96.05%, average values precision, recall, F1-score, AUC 0.97, 0.95, 0.94, respectively, seven-class classification. exhibited exceptional performance multi-class classifications, achieving rates 100%, 99.65%, 99.21%, 98.67%, 97.47% two, three, four, five, six-class respectively. novel not only surpasses many pre-existing (SOTA) methodologies also sets new standard lung-affected using data. Furthermore, integration XAI techniques such as SHAP Grad-CAM enhanced transparency interpretability model’s predictions. findings hold immense promise accelerating improving confidence diagnostic decisions field identification.

Язык: Английский

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ApneaNet: A hybrid 1DCNN-LSTM architecture for detection of Obstructive Sleep Apnea using digitized ECG signals

Biomedical Signal Processing and Control, Год журнала: 2023, Номер 84, С. 104754 - 104754

Опубликована: Март 3, 2023

Язык: Английский

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A novel lightweight CNN for chest X-ray-based lung disease identification on heterogeneous embedded system

Applied Intelligence, Год журнала: 2024, Номер 54(6), С. 4756 - 4780

Опубликована: Март 1, 2024

Abstract The global spread of epidemic lung diseases, including COVID-19, underscores the need for efficient diagnostic methods. Addressing this, we developed and tested a computer-aided, lightweight Convolutional Neural Network (CNN) rapid accurate identification diseases from 29,131 aggregated Chest X-ray (CXR) images representing seven disease categories. Employing five-fold cross-validation method to ensure robustness our results, CNN model, optimized heterogeneous embedded devices, demonstrated superior performance. It achieved 98.56% accuracy, outperforming established networks like ResNet50, NASNetMobile, Xception, MobileNetV2, DenseNet121, ViT-B/16 across precision, recall, F1-score, AUC metrics. Notably, model requires significantly less computational power only 55 minutes average training time per fold, making it highly suitable resource-constrained environments. This study contributes developing efficient, in medical image analysis, underscoring their potential enhance point-of-care processes.

Язык: Английский

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CCTCOVID: COVID-19 detection from chest X-ray images using Compact Convolutional Transformers

Frontiers in Public Health, Год журнала: 2023, Номер 11

Опубликована: Фев. 27, 2023

COVID-19 is a novel virus that attacks the upper respiratory tract and lungs. Its person-to-person transmissibility considerably rapid this has caused serious problems in approximately every facet of individuals' lives. While some infected individuals may remain completely asymptomatic, others have been frequently witnessed to mild severe symptoms. In addition this, thousands death cases around globe indicated detecting an urgent demand communities. Practically, prominently done with help screening medical images such as Computed Tomography (CT) X-ray images. However, cumbersome clinical procedures large number daily imposed great challenges on practitioners. Deep Learning-based approaches demonstrated profound potential wide range tasks. As result, we introduce transformer-based method for automatically from using Compact Convolutional Transformers (CCT). Our extensive experiments prove efficacy proposed accuracy 99.22% which outperforms previous works.

Язык: Английский

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ResNetFed: Federated Deep Learning Architecture for Privacy-Preserving Pneumonia Detection from COVID-19 Chest Radiographs

Journal of Healthcare Informatics Research, Год журнала: 2023, Номер 7(2), С. 203 - 224

Опубликована: Июнь 1, 2023

Personal health data is subject to privacy regulations, making it challenging apply centralized data-driven methods in healthcare, where personalized training frequently used. Federated Learning (FL) promises provide a decentralized solution this problem. In FL, siloed used for the model ensure privacy. paper, we investigate viability of federated approach using detection COVID-19 pneumonia as use case. 1411 individual chest radiographs, sourced from public repository COVIDx8 are The dataset contains radiographs 753 normal lung findings and 658 related pneumonias. We partition unevenly across five separate silos order reflect typical FL scenario. For binary image classification analysis these propose ResNetFed, pre-trained ResNet50 modified federation so that supports Differential Privacy. addition, customized strategy with radiographs. experimental results show ResNetFed clearly outperforms locally trained models. Due uneven distribution silos, observe models perform significantly worse than (mean accuracies 63% 82.82%, respectively). particular, shows excellent performance underpopulated achieving up +34.9 percentage points higher accuracy compared local Thus, can assist initial screening medical centers privacy-preserving manner.

Язык: Английский

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A deep ensemble learning framework for COVID-19 detection in chest X-ray images

Network Modeling Analysis in Health Informatics and Bioinformatics, Год журнала: 2024, Номер 13(1)

Опубликована: Май 31, 2024

Язык: Английский

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CJT-DEO: Condorcet’s Jury Theorem and Differential Evolution Optimization based ensemble of deep neural networks for pulmonary and Colorectal cancer classification

Applied Soft Computing, Год журнала: 2022, Номер 132, С. 109872 - 109872

Опубликована: Ноя. 26, 2022

Язык: Английский

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Review on chest pathogies detection systems using deep learning techniques

Artificial Intelligence Review, Год журнала: 2023, Номер 56(11), С. 12607 - 12653

Опубликована: Март 20, 2023

Язык: Английский

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Ensembled Deep Convolutional Generative Adversarial Network for Grading Imbalanced Diabetic Retinopathy Recognition

IEEE Access, Год журнала: 2023, Номер 11, С. 120554 - 120568

Опубликована: Янв. 1, 2023

Diabetic Retinopathy (DR) is one of the leading causes blindness and vision loss worldwide. According to International Diabetes Federation (IDF), approximately one-third individuals with diabetes, equivalent 32.2%, are affected by some form DR. Due uneven data distribution, intra-class variance, a dearth ophthalmologists, DR diagnosis considered challenging. In recent years, Convolutional Neural Networks (CNN) supervised learning techniques have been potentially useful in computer applications. However, unsupervised CNN has received less attention. Moreover, it more manageable use synthetic images for model training advancements graphics. Therefore, proposed method combines actual augmented views using Deep Generative Adversarial Network (DCGAN) algorithm. The generated implemented balance minority class imbalanced dataset. Furthermore, novel ensemble convolutional neural network algorithm named Different View Ensemble (DVE) that merges weighted average prediction CNN, CNN-i, CNN+i algorithms proposed. evaluated on DDR EyePACS datasets, its performance compared K-Means, Fuzzy C-Means (FCM), Autoencoder-based Embedded Clustering Techniques (DEC). results demonstrate superiority algorithm, achieving an accuracy rate 97.4%, specificity 99.6%, sensitivity 92.3%. promising underscore potential impact this methodology enhancing reliability automated diagnostic systems field ophthalmology. Notably, evaluation considers DCGAN-balanced dataset, where approach exhibits even better balanced classes.

Язык: Английский

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