A Hybrid Sparrow-Chicken Swarm Optimized Deep Convolutional Neural Network achieved 98.5% precision in detecting cardiac afflictions from ECG imagery.
A novel deep learning model utilizing a Hybrid Sparrow-Chicken Swarm Optimized Deep Convolutional Neural Network achieved 98.5% precision in detecting cardiac afflictions from ECG images.
This research introduces a novel technique for early prediction of cardiac affliction in ECG imagery. The initial phase involves pre-processing using an Adaptive Wiener Filter. Subsequently, segmentation is performed utilizing a Modified Fuzzy C-Mean to isolate distinct components of the ECG signal. The next crucial step involves feature extraction deploying Gray Level Co-occurrence Matrix, enabling the capture of texture and spatial dependencies. The classification stage, utilizes Hybrid Sparrow-Chicken Swarm Optimized Deep Convolutional Neural Network, tailored to optimize the deep learning model. The execution with the Python software, achieves 98.5% precision in the detection of cardiac afflictions from ECG imagery.
B. et al. (Wed,) conducted a other in Cardiac affliction. Hybrid Sparrow-Chicken Swarm Optimized Deep Convolutional Neural Network was evaluated on Precision in the detection of cardiac afflictions from ECG imagery. A Hybrid Sparrow-Chicken Swarm Optimized Deep Convolutional Neural Network achieved 98.5% precision in detecting cardiac afflictions from ECG imagery.