Adopting Image Classification Using Pretrained Models with DCNN to Segment an Image in General 2-D Echo Cardiograph Images - Sudheer, P.; Kirubagari, B.; Giri, A. Annamalai (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The biggest cause of death worldwide is cardiovascular disease. Heart attacks and strokes can be avoided with early diagnosis and knowledge of the disease’s trajectory. Early diagnosis of cardiac irregularities is made possible with the help of electrocardiogram (2D Echo) signals, which non-invasively monitor heart activity. Massive amounts of 2D Echo data can be difficult and error-prone to manually examine, which is why researchers are looking into automated interpretation methods to help clinicians make quick and correct choices. In order to monitor patients with cardiac disease and enable early detection and arrhythmia categorization, smart healthcare equipment are essential. However, because of non-linearity and weak amplification, categorizing 2D Echo recordings is difficult. The performance of traditional machine-learning (ML) classifiers when processing large-dimensional data with poorly modelled relationships is questionable. In order to address the limitations of ML classifiers, this study suggests an automated approach combining ResNet18, SegNet, Mobile-Net segmentation, and Feature extraction approaches with Dynamic CNN (D-CNN) classification. Tests using Stanford University’s echo net-dynamic dataset show a considerable improvement in classifier performance, outperforming more sophisticated techniques with over 99.92% accuracy and 99.81% sensitivity. This strategy has the potential to help medical practitioners make quicker and more precise therapy decisions for individuals with heart disease.