Deep Learning is widely used for image classification. Its success heavily relies on data which contains a sufficient amount of region of interest (~10%). However, due to the region of interest in ...

This project demonstrates how to build an image classification model using Convolutional Neural Networks (CNNs) to classify images into predefined categories. It covers data preprocessing, model ...

This article explores some of the most influential deep learning architectures: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), ...

This study presents a deep Convolutional Neural Network (CNN) model for automated COVID-19 RATD image classification. Methods: To address the absence of a RATD image dataset, we crowdsourced 900 ...

The proposed deep-learning algorithm detects three different diseases from features extracted from Optical Coherence Tomography (OCT) images. The deep-learning algorithm uses CNN to classify OCT ...

Classify crack images and explain why using MATLAB [English] This demo shows how to fine-tune a pretrained deep convolutional network called SqueezeNet [1] to perform a crack/normal image ...

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 ...