عنوان مقاله [English]
The purpose of this study is to improve the classification of new methods using a multi-layered model to address retinal diseases diagnosis. This paper presents a multi-layer dictionary learning method for classification tasks. Our multi-layer framework uses a label consistent in K-SVD algorithm to learn a discriminative dictionary for sparse coding in order to learn better features in retinal optical coherence tomography images. In addition to using class labels of training data, we also associate label information with each dictionary item (columns of the dictionary matrix) to enforce discrimination in sparse codes during dictionary learning process. In fact, it relies on a succession of sparse coding and pooling steps in order to find an effective representation of data for classification. Moreover, we apply Duke dataset for validating our algorithm. Our classifier leads to a correct classification rate of 95.85% and 100.00% for normal and abnormal (DME and AMD). Experimental results demonstrate that our algorithm outperforms compared to many recent proposed supervised dictionary learning and sparse representation techniques.
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