remote sensing can be used as a powerful tool by using data from different sources and combine them for vegetation and land cover classification. pasture type classification provides key information for analysis of agricultural productivity, carbon accounting and biodiversity. the first data set that used in this study landsat tm (thematic mapper) optical image and the second envisat asar radar...

Training deep learning models on medical datasets that perform well for all classes is a challenging task. It often the case suboptimal performance obtained some due to natural class imbalance issue comes with data. An effective way tackle this problem by using targeted active learning, where we iteratively add data points belong rare classes, training However, existing methods are ineffective ...

Would you trust physicians if they cannot explain their decisions to you? Medical diagnostics using machine learning gained enormously in importance within the last decade. However, without further enhancements many state-of-the-art methods are not suitable for medical application. The most important reasons insufficient data set quality and black-box behavior of algorithms such as Deep Learnin...

Medical image classification plays an essential role in disease diagnosis and clinical treatment. More more research efforts have been dedicated to the design of effective methods for medical classification. As framework, capsule network (CapsNet) can realize translation equivariance. Lots current applies networks analysis. In this paper, we propose attentive octave convolutional (AOC-Caps) AOC...

Classification of medical images is an important issue in computer-assisted diagnosis. In this paper, a classification scheme based on a one-class kernel principle component analysis (KPCA) model ensemble has been proposed for the classification of medical images. The ensemble consists of one-class KPCA models trained using different image features from each image class, and a proposed product ...

Medical image classification and retrieval systems have been finding extensive use in the areas of image classification according to imaging modalities, body part and diseases. One of the major challenges in the medical classification is the large size images leading to a large number of extracted features which is a burden for the classification algorithm and the resources. In this paper, it i...

In this paper, Support Vector Machine (SVM) was used to learn image feature characteristics for image classification. Several image visual features describe the shape, edge, and texture of image (including histogram, spatial layout, coherence moment and gabor features) have been employed in this paper to categorize the 500 test images into 46 classes. The result shows that the spatial relations...