Abstract
As images are of large size and require huge bandwidth and large storage space, an effective compression algorithm is essential. Hence in this paper, feedforward backpropagation neural network with the multilayer perception using resilient backpropagation (RP) algorithm is used with the objective to develop an image compression in the field of biomedical sciences. With the concept of neural network, data compression can be achieved by producing an internal data representation. This network is an application of backpropagation that takes huge content of data as input, compresses it while storing or transmitting, and decompresses the compressed data whenever required. The training algorithm and development architecture give less distortion and considerable compression ratio and also keep up the capability of hypothesizing and are becoming important. The efficiency of the RP is evaluated on x-ray image of rib cage and has given better results of the various performance metrics when compared to the other algorithms.
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Sasibhushana Rao, G., Vimala Kumari, G., Prabhakara Rao, B. (2019). Image Compression Using Neural Network for Biomedical Applications. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 817. Springer, Singapore. https://doi.org/10.1007/978-981-13-1595-4_9
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DOI: https://doi.org/10.1007/978-981-13-1595-4_9
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