BM3D filtering with Ensemble Hilbert-Huang Transform and spiking neural networks for cardiomegaly detection in chest radiographs.

Cardiomyopathy is a life-threatening condition associated with heart failure, arrhythmias, thromboembolism, and sudden cardiac death, posing a significant contribution to worldwide morbidity and mortality. Cardiomegaly, which is usually the initial radiologic sign, may reflect the progression of an underlying heart disease or an underlying undiagnosed cardiac condition. Chest radiography is the most frequently used imaging method for detecting heart enlargement. Prompt and accurate diagnosis is essential for prompt intervention and appropriate treatment planning to prevent disease progression and improve patient outcomes. The current work provides a new methodology for automated cardiomegaly diagnosis using X-ray images through the fusion of Block-Matching and 3D Filtering (BM3D) within the Ensemble Hilbert-Huang Transform (EHHT), convolutional neural networks like Pretrained VGG16, ResNet50, InceptionV3, DenseNet169, and Spiking Neural Networks (SNN), and Classifiers. BM3D is first used for image edge retention and noise reduction, and then EHHT is applied to obtain informative features from X-ray images. The features that have been extracted are then processed using an SNN that simulates neural processes at a biological level and offers a biologically possible classification solution. Gradient-weighted Class Activation Mapping (GradCAM) emphasized important areas that affected model predictions. The SNN performed the best among all the models tested, with 97.6 % accuracy, 96.3 % sensitivity, and 98.2 % specificity. These findings show the SNN's high potential for facilitating accurate and efficient cardiomyopathy diagnosis, leading to enhanced clinical decision-making and patient outcomes.