In this article, a new classification model based on covariance matrices is built in order to classify buried objects. The inputs of the proposed models are the hyperbola thumbnails obtained with a ...

Their proposed model achieved a validation accuracy of 50% and a final test accuracy of 48.2%. All these previous studies have applied deep learning techniques to detect and classify diabetic ...

The results highlight the importance of incorporating cross entropy alongside traditional metrics for a more comprehensive evaluation of deep learning models in medical image classification, providing ...

The core of this project involves building a Convolutional Neural Network (CNN) using a deep learning framework. The CNN architecture is designed to extract features from the images and classify them ...

Moreover, the COVID-19 images are then classified as either positive or negative using a Duffing Equation Tuna Swarm (DETS)-optimized Resnet 101 classifier trained on synthetic and real images from ...

Deep learning techniques like Convolutional Neural Networks (CNNs) have proven to be especially powerful in tasks such as image classification, object detection, and semantic segmentation.

Convolutional Neural Network (CNN) has made outstanding achievements in image processing and detection. The recent research uses CNN to classify the medical images, but this performance depends on its ...

Classify images using deep learning algorithms Most computer vision algorithms use a convolution neural network, or CNN. Like basic feedforward neural networks, CNNs learn from inputs, adjusting their ...

Marzullo et al. (2019) used the graph CNN model to classify MS patients into four clinical profiles (clinically isolated syndrome, relapsing-remitting, secondary-progressive, and primary-progressive) ...