Classification of Shoulder X-ray Images with Deep Learning Ensemble Models

Using deep learning on X-ray images to detect threats

Deep Transfer Learning Ensemble for Classification

Transfer learning algorithms typically assume that the training data and the test data come from different distribution. It is better at adapting to learn new tasks and concepts more quickly and accurately by exploiting previously gained knowledge. Deep Transfer Learning (DTL) emerged as a new paradigm in transfer learning in which a deep model offer greater flexibility in extracting high-level...

Classification of Chest Radiology Images in Order to Identify Patients with COVID-19 Using Deep Learning Techniques

Background and Aim: Due to the important role of radiological images for identifying patients with COVID-19, creating a model based on deep learning methods was the main objective of this study. Materials and Methods: 15,153 available chest images of normal, COVID-19, and pneumonia individuals which were in the Kaggle data repository was used as dataset of this research. Data preprocessing inc...

Automatic Classification of Medical X-ray Images

Image representation is one of the major aspects of automatic classification algorithms. In this paper, different feature extraction techniques have been utilized to represent medical X-ray images. They are categorized into two groups; (i) low-level image representation such as Gray Level Co-occurrence Matrix(GLCM), Canny Edge Operator, Local Binary Pattern(LBP) , pixel value, and (ii) local pa...

Multi-label Classification of Satellite Images with Deep Learning

Up-to-date location information of human activity is vitally important to scientists and governments working to preserve the Amazon rainforest. We implement a Convolutional Neural Network (CNN) model to perform multi-label classification of Amazon satellite images. Our model identifies the weather conditions and natural terrain features in the images as well as man-made developments such as roa...