An efficient multilayer deep detection perceptron (MLDDP) methodology for detecting testicular anomalies with or without congenital heart disease (TACHD)

Abstract

In the real world, the detection of heart diseases is a challenging process. For detecting testicular anomalies with or without congenital heart disease, 2D ultrasound images through a computer-aided diagnostic support system are favorable. Existing work achieves classification through an adaptive neuro-fuzzy inference system (ANFIS) with an F1 score of 0.9673. Although the classification accuracy is good, there are some small changes in detection, leading to misclassification in some miniature valve detection scenarios. To increase the efficiency, a multilayer deep detection perceptron that over performs an ANFIS, a radial basis function neural network, and a multilayer back propagation neural network is proposed. It has the capability of optimizing the classification with higher performance accuracy. During the valuable phases of processing, classification of prenatal ventricular septal defects was achieved with help of the novel multilayer perceptron. Further, gradient descent is used to increase efficiency and improve the filter rate by using a linear algebra activation function. Both backpropagation and the least squares ensured that the smallest error occurred in classification and detection. The accuracy of this proposed method reaches 98%.