Parametric regularization loss in super-resolution reconstruction

Viriyavisuthisakul, Supatta; Kaothanthong, Natsuda; Sanguansat, Parinya; Nguyen, Le-Minh; Haruechaiyasak, Choochart

doi:10.1007/s00138-022-01315-9

Cited by 4 publications

(2 citation statements)

References 42 publications

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“…4,5 However, all available imaging modalities need higher costs and well-trained operators. 6 In medical imaging, image classification is mainly used. 7 Deep learning has become widely used as a classification approach due to its automatic feature extraction and calculation within the deep system's convolutional layers.…”

Section: Introductionmentioning

confidence: 99%

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Multi‐branch sustainable convolutional neural network for disease classification

Naz

Shah

Khattak

et al. 2023

Int J Imaging Syst Tech

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Pandemic and natural disasters are growing more often, imposing even more pressure on life care services and users. There are knowledge gaps regarding how to prevent disasters and pandemics. In recent years, after heart disease, corona virus disease‐19 (COVID‐19), brain stroke, and cancer are at their peak. Different machine learning and deep learning‐based techniques are presented to detect these diseases. Existing technique uses two branches that have been used for detection and prediction of disease accurately such as brain hemorrhage. However, existing techniques have been focused on the detection of specific diseases with double‐branches convolutional neural networks (CNNs). There is a need to develop a model to detect multiple diseases at the same time using computerized tomography (CT) scan images. We proposed a model that consists of 12 branches of CNN to detect the different types of diseases with their subtypes using CT scan images and classify them more accurately. We proposed multi‐branch sustainable CNN model with deep learning architecture trained on the brain CT hemorrhage, COVID‐19 lung CT scans and chest CT scans with subtypes of lung cancers. Feature extracted automatically from preprocessed input data and passed to classifiers for classification in the form of concatenated feature vectors. Six classifiers support vector machine (SVM), decision tree (DT), K‐nearest neighbor (K‐NN), artificial neural network (ANN), naïve Bayes (NB), linear regression (LR) classifiers, and three ensembles the random forest (RF), AdaBoost, gradient boosting ensembles were tested on our model for classification and prediction. Our model achieved the best results on RF on each dataset. Respectively, on brain CT hemorrhage achieved (99.79%) accuracy, on COVID‐19 lung CT scans achieved (97.61%), and on chest CT scans dataset achieved (98.77%).

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Section: Introductionmentioning

confidence: 99%

“…X‐ray imaging, functional MRI, magnetoencephalography, diffuse optical imaging, and positron emission tomography are also imaging modalities available for brain analysis 4,5 . However, all available imaging modalities need higher costs and well‐trained operators 6 . In medical imaging, image classification is mainly used 7 .…”

Section: Introductionmentioning

confidence: 99%