DICOM images which helps in diagnosis and prognosis would be critical component in health care systems. Speedy recovery of past historic DICOM images based on the given query image is becoming a critical requirement for the Laboratories and Doctors for quick inference and accurate analogy of the patient conditions. In existing, It is also identified that there is a presence of imbalanced data set which degrade the retrieval accuracy of the model which may reduce by using extract the different kinds of features. The DCNN classifiers are trained by datasets whose data distributions of individual classes are not even or similar, they have always suffered from imbalanced classification performance against classes. Through DCNN can be used to minimize the gaps in terms of accuracy and retrieval but still efficiency parallelization would be essential for faster training and retrieval time. Time complexity is always been a major issue in DCNN, to overcome the above complexity the parallelization of model or data dimension need to be adapted. In this paper, parallel deep convolutional neural network (PDCNN) model is proposed by hyper parameter optimimzation for CBMIR system. The proposed model incorporating the low level content features, high level semantic features and compact features along with DCNN features to tackle the imbalanced dataset problem and reducing the DCNN training time for DICOM images. The high-level and compact features are extracted to resolve the imbalanced dataset problem by using the following algorithms: (a) local binary pattern (LBP), (b) histogram of oriented gradients (HOG) and (c) radon. The data parallelism was adopted in the proposed DCNN model to reduce the network training time by execution of DCNN layers across multiple CPU cores on a single PC. The implementation results for the proposed model in terms of Precision, Recall and F measure values are 87%, 87% and 92% respectively.
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Haripriya, P., Porkodi, R. Parallel deep convolutional neural network for content based medical image retrieval. J Ambient Intell Human Comput 12, 781–795 (2021). https://doi.org/10.1007/s12652-020-02077-w
- Deep convolutional neural network
- Deep learning