Using Frost Filterative Fuzzified Gravitational Search-Based Shift Invariant Deep Feature Learning with Blockchain for Distributed Pattern Recognition

Echocardiogram is the test which uses ultrasound to visualize the various heart-related diseases. In order to improve the pattern recognition accuracy, the Frost-Filterative Fuzzified Gravitational Search-based Shift Invariant Deep Structure Feature Learning (FFFGS-SIDSFL) technique is introduced. The FFFGS-SIDSFL technique takes the echocardiogram videos as input for pattern recognition. The input echocardiogram videos are partitioned into frames. At first, the enhanced frost filtering technique is applied to a frame for removing the speckle noise and increase the quality of image. Second, an optimal combination of the feature selection is performed by applying Stochastic Gradient Learning Fuzzified Gravitational Search algorithm. The fuzzy triangular membership function is applied to enhance the Gravitational Search algorithm. Followed by, the different statistical features such as texture, shape, size and intensity are extracted. Finally, the Gaussian activation function at the output unit is used for matching the learned feature vector with the training feature vector. The matching results provide the accurate pattern recognition. Experimental measurement is conducted for analyzing the performance of FFFGS-SIDSFL technique against the two state-of-the-art methods with different metrics, such as Peak signal to noise ratio, pattern recognition accuracy, computational time, and complexity with respect to a diverse number of electrocardiogram images. Based on this observation, the FFFGS-SIDSFL technique provides the better performance in terms of higher accuracy results than the two other existing approaches. As a future work, a distributed pattern recognition scheme that uses IoT with blockchain as event monitoring is proposed.