Akademik Veri Yönetim Sistemi
Expert Systems with Applications, cilt.161, 2020 (SCI-Expanded)
Content-based medical image retrieval (CBMIR) is one of the most challenging and ambiguous tasks used to minimize the semantic gap between images and human queries in datasets with rich information content. Similar to the human visual saliency mechanism, CBMIR systems also use the visual features in the images for searching purposes. As a result of this search process, automatically accessing the images is very convenient in large and balanced datasets. Still, it is generally not possible to find such datasets in the medical domain. In this study, a four-step and effective hash code generation technique is presented to reduce the semantic gap between low-level features and high-level semantics for unbalanced medical image datasets. In the first stage, the convolutional neural network (CNN) architecture, the most effective feature representation method available today, is employed to extract discriminative features from images automatically. The features obtained in the last fully connected layer (FCL) at the output of the CNN architecture are used for hash code generation. In the second stage, using the Synthetic Minority Over-sampling Technique (SMOTE), the imbalance between the classes in the dataset is reduced. The solution to the unbalanced problem increases performance by almost 3%. In the third stage, balanced features are converted to a code of 13 symbols by using deep stacked auto-encoder. Finally, this code is translated to the standard 13-character labeling and retrieval code used by the 'Image retrieval in the medical application' (IRMA) dataset, since this is the database with which experiments have been done. IRMA error parameter, classification performance, and retrieval performance of the proposed method are more successful than other state-of-the-art methods.