Empirical mode Decomposition and Dual Sigmoid Activation Function-Based Faster RCNN for Big Data Doppler Scan Image Classification
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Abstract
This paper proposes a big data Doppler scan image classification system that uses empirical mode decomposition (EMD) and dual sigmoid activation function-based faster region convolutional neural network (R-CNN). This approach initially arranges the pixels of the Doppler scan images in a zig-zag order sequence. This one-dimensional sequence is decomposed to L number of intrinsic mode function (IMF) using the EMD algorithm. The spectrums of the decomposed IMF are estimated using the one-dimensional Fourier transforms. Required IMFs are then selected based on the frequency spacing estimated on the Fourier spectrum. The resultant image is then reconstructed using the selected IMF’s. The resultant Doppler scan image has less redundant information and is trained using a faster RCNN algorithm. Instead of using the traditional activation functions, the proposed faster RCNN uses a dual sigmoid activation function that classifies the Doppler images into five classes. The classes are namely Maternal cervix, Thorax, Femur, Brain, Abdomen, and other regions. The experimental evaluation uses the parametersnamelyF1 score, specificity, accuracy, sensitivity, precision, and time complexity with the big data Doppler ultrasound scan image dataset that contains 12400 images collected from 1792 patients.
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