Problem statement: To meet the demand for high speed transmission of image in efficient image storage and remote medical treatment, the efficient image compression is essential. The contourlet transform along with wavelet theory has great potential in medical image compression. Approach: The significant portion of the medical image applied with Fuzzy C-means based contourlet transform. DWT applied to the rest of the image. Finally modified EZW of six symbols differing from normal EZW was applied to the whole image. This technique increases PSNR and gives better compression ratio. Results: The MATLAB simulation showed that the method of separate transforms to the two regions proves better results compared to the ordinary way of applying only single transforms to the whole image. The results revealed that proposed algorithm was simple and computationally fewer complexes based on embedded block coding with coefficient truncation. Conclusion:The compression of the proposed algorithm is superior to EZW, SPIHT. Our new method of compression algorithm can be used to improve the performance of Compression Ratio (CR) and Peak Signal to Noise Ratio (PSNR). In future this study can be extended to real time applications for video compression in medical images.

Heart disease identification is one of the most challenging task that requires highly experienced cardiologists. However, in developing nations such as Ethiopia, there are a few cardiologists and heart disease detection is more challenging. As an alternative solution to cardiologist, this study proposed a more effective model for heart disease detection by employing random forest and sequential feature selection (SFS). SFS is an effective approach to improve the performance of random forest model on heart disease detection. SFS removes unrelated features in heart disease dataset that tends to mislead random forest model on heart disease detection. Thus, removing inappropriate and duplicate features from the training set with sequential feature selection approach plays significant role in improving the performance of the proposed model. The proposed feature selection approach is evaluated using real world clinical heart disease dataset collected from University of California Irvine (UCI) data repository. Empirical test on validation set reveals that the proposed model performs well as compared to the existing methods. Overall, the state of-the-art heart disease detection model with classification accuracy of 98.53% is proposed for heart disease detection using SFS and random forest model.

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