Classification of Heart Diseases using Fusion Based Learning Approach
Keywords:
Ultrasound image analysis, fusion-based model, VGG19, discrete wavelet transform, DWT, 3D-Multi-Chamber Segmentation, Ensemble classificationAbstract
Tetralogy of Fallot (TOF) is a cardiac anomaly characterized by the coexistence of four related heart defects. TOF is most common in children. TOF symptoms include Down syndrome, Alagille syndrome, and DiGeorge syndrome (which causes heart defects, low calcium levels, and poor immune function). A higher risk of getting an infection of the layers of the heart is called endocarditic. This paper presents a novel fusion-based classification model for analyzing ultrasound images. The proposed model integrates multiple components, including the pre-trained VGG19 model, discrete wavelet transform (DWT) for pre-processing, and advanced segmentation models such as 3D-Multi-Chamber Segmentation for region segmentation. An Ensemble classification approach is employed for classifying normal and abnormal ultrasound images. The pre-trained VGG19 model is utilized as a feature extractor to capture high-level features from the ultrasound images. These features are then enhanced using DWT, which effectively decomposes the images into different frequency bands, providing a multi-resolution representation. The model can effectively capture local and global image characteristics by incorporating DWT. To segment the ultrasound images into other regions, the 3D-Multi-Chamber Segmentation model is employed. This segmentation approach leverages the three-dimensional nature of ultrasound images to accurately delineate areas of interest, such as chambers in the heart or structures in the abdomen. The segmented regions provide valuable information for subsequent classification. An Ensemble approach is adopted for the classification task to make accurate predictions regarding the normalcy or abnormality of ultrasound images. The Ensemble classification combines the outputs of multiple classification models, which enhances the overall robustness and performance of the classification process. The proposed fusion-based model offers a comprehensive ultrasound image analysis solution, leveraging various components' strengths. It achieves accurate and reliable classification results by integrating pre-trained models, DWT pre-processing, and advanced segmentation techniques. Experiments conducted on ultrasound images that show the comparative performance of list of algorithms.
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