| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
A dark and bright channel prior guided deep network for retinal image quality assessment
|
Xu, Ziwen
|
|
|
42 |
3 |
p. 772-783
|
artikel
|
| 2 |
A novel end-to-end deep learning approach for cancer detection based on microscopic medical images
|
Hammad, Mohamed
|
|
|
42 |
3 |
p. 737-748
|
artikel
|
| 3 |
Applications of machine-learning algorithms for prediction of benign and malignant breast lesions using ultrasound radiomics signatures: A multi-center study
|
Homayoun, Hassan
|
|
|
42 |
3 |
p. 921-933
|
artikel
|
| 4 |
Assessment of CT for the categorization of hemorrhagic stroke (HS) and cerebral amyloid angiopathy hemorrhage (CAAH): A review
|
Sudarshan, Vidya K.
|
|
|
42 |
3 |
p. 888-901
|
artikel
|
| 5 |
Automated diagnosis of COVID stages from lung CT images using statistical features in 2-dimensional flexible analytic wavelet transform
|
Patel, Rajneesh Kumar
|
|
|
42 |
3 |
p. 829-841
|
artikel
|
| 6 |
Cell image augmentation for classification task using GANs on Pap smear dataset
|
Zak, Jakub
|
|
|
42 |
3 |
p. 995-1011
|
artikel
|
| 7 |
Classification of breast cancer from histopathology images using an ensemble of deep multiscale networks
|
Karthik, R.
|
|
|
42 |
3 |
p. 963-976
|
artikel
|
| 8 |
Classification of mild and severe adolescent idiopathic scoliosis (AIS) from healthy subjects via a supervised learning model based on electromyogram and ground reaction force data during gait
|
Sikidar, Arnab
|
|
|
42 |
3 |
p. 870-887
|
artikel
|
| 9 |
COVID-RDNet: A novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images
|
Fang, Lingling
|
|
|
42 |
3 |
p. 977-994
|
artikel
|
| 10 |
Detection of pneumonia using convolutional neural networks and deep learning
|
Szepesi, Patrik
|
|
|
42 |
3 |
p. 1012-1022
|
artikel
|
| 11 |
Diagnosis of Parkinson's disease based on SHAP value feature selection
|
Liu, Yuchun
|
|
|
42 |
3 |
p. 856-869
|
artikel
|
| 12 |
Dual-modality synthetic mammogram construction for breast lesion detection using U-DARTS
|
Rautela, Kamakshi
|
|
|
42 |
3 |
p. 1041-1050
|
artikel
|
| 13 |
EEG_GENet: A feature-level graph embedding method for motor imagery classification based on EEG signals
|
Wang, Huiyang
|
|
|
42 |
3 |
p. 1023-1040
|
artikel
|
| 14 |
Enhanced decision tree induction using evolutionary techniques for Parkinson's disease classification
|
Ghane, Mostafa
|
|
|
42 |
3 |
p. 902-920
|
artikel
|
| 15 |
Explainable COVID-19 detection using fractal dimension and vision transformer with Grad-CAM on cough sounds
|
Sobahi, Nebras
|
|
|
42 |
3 |
p. 1066-1080
|
artikel
|
| 16 |
Hyp-Net: Automated detection of hypertension using deep convolutional neural network and Gabor transform techniques with ballistocardiogram signals
|
Gupta, Kapil
|
|
|
42 |
3 |
p. 784-796
|
artikel
|
| 17 |
Multi-class nucleus detection and classification using deep convolutional neural network with enhanced high dimensional dissimilarity translation model on cervical cells
|
Karri, Meghana
|
|
|
42 |
3 |
p. 797-814
|
artikel
|
| 18 |
Novel multiple pooling and local phase quantization stable feature extraction techniques for automated classification of brain infarcts
|
Dogan, Sengul
|
|
|
42 |
3 |
p. 815-828
|
artikel
|
| 19 |
Predicting hospital emergency department visits with deep learning approaches
|
Zhao, Xinxing
|
|
|
42 |
3 |
p. 1051-1065
|
artikel
|
| 20 |
Prospects for the application of aptamer based assay platforms in pathogen detection
|
Banu, Kauser
|
|
|
42 |
3 |
p. 934-949
|
artikel
|
| 21 |
Textural feature of EEG signals as a new biomarker of reward processing in Parkinson’s disease detection
|
Ezazi, Yasamin
|
|
|
42 |
3 |
p. 950-962
|
artikel
|
| 22 |
The internet of medical things and artificial intelligence: trends, challenges, and opportunities
|
Kakhi, Kourosh
|
|
|
42 |
3 |
p. 749-771
|
artikel
|
| 23 |
TL-med: A Two-stage transfer learning recognition model for medical images of COVID-19
|
Meng, Jiana
|
|
|
42 |
3 |
p. 842-855
|
artikel
|
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