DeepCardioNet: Efficient Left Ventricular Epicardium and Endocardium Segmentation using Computer Vision

In the realm of medical image analysis, accurate segmentation of cardiac structures is essential for accurate diagnosis and therapy planning. Using the efficient Attention Swin U-Net architecture, this study provides DEEPCARDIONET, a novel computer vision approach for effectively segmenting the left ventricular epicardium and endocardium. The paper presents DEEPCARDIONET, a cutting-edge computer vision method designed to efficiently separate the left ventricular epicardium and endocardium in medical pictures. The main innovation of DEEPCARDIONET is that it makes use of the Attention Swin U-Net architecture, a state-of-the-art framework that is well-known for its capacity to collect contextual information and complicated attributes. Specially designed for the segmentation task, the Attention Swin U-Net guarantees superior performance in identifying the relevant left ventricular characteristics. The model's ability to identify positive instances with high precision and a low false positive rate is demonstrated by its good sensitivity, specificity, and accuracy. The Dice Similarity Coefficient (DSC) illustrates the improved performance of the proposed method in addition to accuracy, showing how effectively it captures spatial overlaps between predicted and ground truth segmentations. The model's generalizability and performance in a variety of medical imaging contexts are demonstrated by its application and evaluation across many datasets. DEEPCARDIONET is an intriguing method for enhancing cardiac picture segmentation, with potential applications in clinical diagnosis and treatment planning. The proposed method achieves an amazing accuracy of 99.21% by using a deep neural network architecture, which significantly beats existing models like TransUNet, MedT, and FAT-Net. The implementation, which uses Python, demonstrates how versatile and useful the language is for the scientific computing community.