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punitha-valli / Wide-Residual-Newtork-

Medical image classification is essential to store and retrieve large amount of data. Also to help doctors assisting instantaneously. Various approaches are provided in literature. Very good results are obtained using fuzzy inference system, some data mining techniques. But the best published results are using Local binary pattern(LBP) and Support vector machine(SVM). In this paper, we try to use a deep learning approach to classify medical images. A technique called WRN( Wide Residual Network) is implemented and tested using Image Retrieval in Medical Applications(IRMA) dataset.

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Wide-Residual-Newtork-

Medical image classification is essential to store and retrieve large amount of data. Also to help doctors assisting instantaneously. Various approaches are provided in literature. Very good results are obtained using fuzzy inference system, some data mining techniques. But the best published results are using Local binary pattern(LBP) and Support vector machine(SVM). In this paper, we try to use a deep learning approach to classify medical images. A technique called WRN( Wide Residual Network) is implemented and tested using Image Retrieval in Medical Applications(IRMA) dataset.

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Medical image classification is essential to store and retrieve large amount of data. Also to help doctors assisting instantaneously. Various approaches are provided in literature. Very good results are obtained using fuzzy inference system, some data mining techniques. But the best published results are using Local binary pattern(LBP) and Support vector machine(SVM). In this paper, we try to use a deep learning approach to classify medical images. A technique called WRN( Wide Residual Network) is implemented and tested using Image Retrieval in Medical Applications(IRMA) dataset.

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