Efficient intelligent compression and recognition system-based vision computing for computed tomography COVID images

Computed tomography (CT) image-based medical recognition is extensively used for COVID as it improves and scanning rate. A method intelligent compression system-based vision computing CT (ICRS-VC-COVID) was developed. The proposed system first preprocesses lung images. Segmentation then to split the image into two regions: nonregion of interest (NROI) with fractal lossy region context tree weighting lossless. Subsequently, a fast discrete curvelet transform (FDCT) applied. Finally, vector quantization implemented through encoder, channel, decoder. Two experiments were conducted test ICRS-VC-COVID. evaluated segmentation compression, FDCT, wavelet transform, (DCT). second DCT segmentation. It demonstrates significant improvement in performance parameters, such mean square error, peak signal-to-noise ratio, ratio. At similar computational complexity, ICRS-VC-COVID superior some existing techniques. Moreover, at same bit rate, significantly quality image. Thus, can enable images be applied disease low power space.