A Health Care Image Compression Scheme using Discrete Wavelet Transform and Convolution Neural Network



Medical image processing is an important field that directly impacts the health care system. It recognizes disease and also provides information for diagnosis and surgical process. The objectives of medical image compression are to reduce the computational complexity, storage size, and transmission bandwidth. This research has proposed an image compression scheme (MIC-DWT-CNN) based on discrete wavelet transform and convolutional neural networks. Region-growing and otsu-thresholding methods have separated the interested area and non-interested area of the medical image. The DWT has compressed the region of interest, and CNN has compressed the non-interested area in the medical image. The MIC-DWT-CNN scheme has experimented on the images of the medical image dataset using the python platform. The research objective is to achieve better compression efficiency and image quality. The performance of the MIC-DWT-CNN method has been evaluated using Mean square error (MSE), Peak Signal to Noise Ratio (PSNR), and Compression Ratio (CR). The existing techniques have been used to compare with the MIC-DWT-CNN method. The MIC-DWT-CNN method has achieved a better compression performance than the existing methods. The MIC-DWT-CNN method has achieved a higher CR, i.e., 25.01, than existing methods. Also, the model has provided the required level of MSE and PSNR values.