… In this paper, hybrid learning models are used to classify COVID-19 CT images, Community-…
with various machine learning classifiers and other deep learning classifiers for better data …

… Finally, hybrid classifiers KNN and FLN voting to classify chest CT … , FLN and KNN
classifiers are hybrid to perform cancer pattern diagnosis at an early stage from CT scan images. …

… In this section, different components of the proposed hybrid model are … Classifier, which
is a crucial component in this study, and; The integration mechanism used to build the hybrid …

… classifier [4] because of its success in interpreting terran satellite images and because of the
similarity of that problem to MR-scan … intricate images, such as cranial tomographic scans. …

… Tomography (CT) scan … classifier and additionally hybrid 3D-CNN was the second proposed
CNN architecture which utilized Radial Basis Function (RBF)-based SVM as its classifier …

… Tomography (CT) scans. In the False Positive Reduction (FPR) step, we used a classifier …
In the second stage, they created a hybrid loss residual network that harnesses the location …

… ) swept-source OCT scan per patient. Convolutional neural networks were used to extract
rich features from maps derived from these scans. Random forest classifier was used to train a …

… Training of classifier in the nodes In this paper, classifiers in the nodes are trained by
image features and the regression tree classification algorithm 19 based on two concepts: the …

… The original CT-scan/Chest-X-ray image is, first, resized according to the input specification
of the CNN classifier. Next, the resized image is converted to a gray-scale image (if it is not …

… In the present study, we propose an improved detection scheme by introducing hybrid
nodule detection and false positive (FP) reduction with three classifiers. We analyzed the …