Domain-ensemble learning with cross-domain mixup for thoracic disease classification in unseen domains

Biomedical Signal Processing and Control, Год журнала: 2022, Номер 81, С. 104488 - 104488

Опубликована: Дек. 14, 2022

Язык: Английский

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Conditional cascaded network (CCN) approach for diagnosis of COVID-19 in chest X-ray and CT images using transfer learning

Biomedical Signal Processing and Control, Год журнала: 2023, Номер 87, С. 105563 - 105563

Опубликована: Окт. 3, 2023

Язык: Английский

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RED-CNN: The Multi-Classification Network for Pulmonary Diseases

Electronics, Год журнала: 2022, Номер 11(18), С. 2896 - 2896

Опубликована: Сен. 13, 2022

Deep learning is a convenient method for doctors to classify pulmonary diseases such as COVID-19, viral pneumonia, bacterial and tuberculosis. However, task requires dataset including samples of all these more effective network capture the features images accurately. In this paper, we propose five-classification disease model, pre-processing input data, feature extraction, classifier. The main points model are follows. Firstly, present new named RED-CNN which based on CNN architecture constructed using RED block. block composed Res2Net module, ECA Double BlazeBlock capable extracting detailed information, providing cross-channel enhancing extraction global information with strong capability. Secondly, by merging two selected datasets, Curated Chest X-Ray Image Dataset COVID-19 tuberculosis (TB) chest X-ray database, five types data: normal, order assess efficiency proposed series experiments were carried out 5-fold cross validation, results accuracy, precision, recall, F1 value, Jaccard scores 91.796%, 92.062%, 91.892%, 86.176%, respectively. Our algorithm performs better than other classification algorithms.

Язык: Английский

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Covid-19 Detection by Machine Learning Using Chest Radiographs

International Journal for Scientific Research, Год журнала: 2024, Номер 3(2), С. 247 - 266

Опубликована: Фев. 29, 2024

The recent pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has highlighted the importance of early detection infections, especially when RT-PCR testing equipment is scarce. This study introduces a machine learning algorithm using CT scan imaging for rapid COVID-19 identification. algorithm, designed as computer-aided model, analyzed 536 images (32x32 pixels) categorized into infected and non-infected groups. model preprocesses Prewitt filter discrete cosine transform, then extracts features through various statistical methods histogram oriented gradients (HOG). Out 32 features, 29 showed high significance (p-value < 0.05), effectively distinguishing normal abnormal cases. These were classified support vector (SVM) k-nearest neighbor (KNN) methods. Performance metrics like sensitivity, specificity, accuracy used to evaluate classifiers. results that classifiers KNN-1, KNN-3, KNN-5, SVM-Linear could distinguish between perfectly (100%) it was applied proposed on tested ROIs images. Also, SVM-RBF had less performance than other with 98.38% but still at high-performance level. indicate physicians can utilize an assisted tool detecting COVID-19.

Язык: Английский

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eFuseNet: A deep ensemble fusion network for efficient detection of Arrhythmia and Myocardial Infarction using ECG signals

Multimedia Tools and Applications, Год журнала: 2024, Номер unknown

Опубликована: Июль 13, 2024

Язык: Английский

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