This paper presents the details of the participation of FCSE (Faculty of Computer Science and Engineering) research team in ImageCLEF 2013 medical tasks (modality classification, ad-hoc image retrieval and case-based retrieval). For the modality classification task we used SIFT descriptors and tf − idf weights of the surrounding text (image caption and paper title) as features. SVMs with χ kern...

The classification system is generally categorized into Neural Network(NN) Classification or Data Mining classification and it comprises the tasks of pre-processing, feature extraction, classification and evaluation. The choice of classification method is related to the classes/groups, patterns/features, feature extraction, feature selection, the selection of training, testing samples and its t...

Advances in the medical imaging technology has lead to an exponential growth in the number of digital images that needs to be acquired, analyzed, classified, stored and retrieved in medical centers. As a result, medical image classification and retrieval has recently gained high interest in the scientific community. Despite several attempts, such as the yearly-held ImageCLEF Medical Image Annot...

MedGIFT is a medical imaging research group of the Geneva University Hospitals and the University of Geneva, Switzerland. Since 2004, the medGIFT group has participated in the ImageCLEF benchmark each year, focusing mainly on the medical imaging tasks. For the medical image retrieval task, two existing retrieval engines were used: the GNU Image Finding Tool (GIFT) as image retrieval engine and ...

Due to the increasing use of digital medical images, a need exists to develop an approach for automatic image annotation, which provides textual labels for images. Thus added labels can be used to access images using textual queries. Automatic image annotation can be separated into two individual tasks: feature extraction and image classification. In this paper, the authors present feature extr...

In this paper, we use Support Vector Machine (SVM) to learn image feature characteristics for assisting the task of image classification. The ImageCLEF 2005 evaluation offers a superior test bed for medical image content retrieval. Several image visual features (including histogram, spatial layout, coherence moment and gabor features) have been employed in this paper to categorize the 1,000 tes...

This research work presents a method for automatic classification of medical images in two classes Normal and Abnormal based on image features and automatic abnormality detection. Our proposed system consists of four phases Preprocessing, Feature extraction, Classification, and Post processing. Statistical texture feature set is derived from normal and abnormal images. We used the KNN classifie...

Deep learning (DL) classification has become a major research topic in the areas of cancer prediction, image cell classification, and medicine. Furthermore, DL is core other subfields. Owing to various forms ensemble models, models have achieved state-of-the-art performances fields such as However, existing cannot solve problem generalization perfectly proposed solutions only for tasks with spe...