Hybrid Pattern Extraction with Deep Learning-Based Heart Disease Diagnosis Using Echocardiogram Images

Abstract

Echocardiography represents a noninvasive diagnostic approach that offers information concerning hemodynamics and cardiac function. It is a familiar cardiovascular diagnostic test apart from chest X-ray and echocardiography. The medical knowledge is enhanced by the Artificial Intelligence (AI) approaches like deep learning and machine learning because of the increase in the complexity as well as the volume of the data that in turn unlocks the clinically significant information. Similarly, the usage of developing information as well as communication technologies is becoming important for generating a persistent healthcare service via which the chronic disease and elderly patients get their medical facility at their home that in turn enhances the life quality and avoids hospitalizations. The main intention of this paper is to design and develop a novel heart disease diagnosis using speckle-noise reduction and deep learning-based feature learning and classification. The datasets gathered from the hospital are composed of both the images and the video frames. Since echocardiogram images suffer from speckle noise, the initial process is the speckle-noise reduction technique. Then, the pattern extraction is performed by combining the Local Binary Pattern (LBP), and Weber Local Descriptor (WLD) referred to as the hybrid pattern extraction. The deep feature learning is conducted by the optimized Convolutional Neural Network (CNN), in which the features are extracted from the max-pooling layer, and the fully connected layer is replaced by the optimized Recurrent Neural Network (RNN) for handling the diagnosis of heart disease, thus proposed model is termed as CRNN. The novel Adaptive Electric Fish Optimization (A-EFO) is used for performing feature learning and classification. In the final step, the best accuracy is achieved with the introduced model, while a comparative analysis is accomplished over the traditional models. From the experimental analysis, FDR of A-EFO-CRNN at 75% learning percentage is 21.05%, 15%, 48.89%, and 71.95% progressed than CRNN, CNN, RNN, and NN, respectively. Thus, the performance of the A-EFO-CRNN is enriched than the existing heuristic-oriented and classifiers in terms of the image dataset.