The Squeeze-ICNN deep learning model achieved 95.6% accuracy, 97.9% precision, and 94.9% sensitivity in detecting cardiovascular disease from ECG images.
Does the Squeeze-ICNN model improve the accuracy of cardiovascular disease detection from ECG images compared to conventional deep learning models?
A novel Squeeze-ICNN deep learning architecture utilizing multi-level feature extraction from ECG images demonstrated high accuracy (95.6%) for automated cardiovascular disease detection.
Abstract Nowadays, the leading causes of early mortality, especially in countries with low or middle incomes are cardiovascular diseases (CVDs), which include heart failure, stroke, hypertension, and coronary artery disease. Premature death rates may be reduced if certain illnesses are identified early. Numerous methods, including data mining and machine learning (ML) have been proposed by researchers for early identification as well as tracking of cardiac patients in the context of CVD prediction. Yet there remains significant concern about the effectiveness of these strategies in scenarios where there is a high error rate and uncertain precision. Thereby, choosing a prediction method that may produce high accuracy and less errors is essential. This article presents the Squeeze-improved convolutional neural network (ICNN) model for detecting CVD via electrocardiogram (ECG) images. During preprocessing, the ECG image noise is reduced using an improved median filter (IMF). The model extracts shape, deep, and local Gabor XOR pattern (LGXP) features. For classification features, SqueezeNet and improved CNN (IMP-CNN) are combined within a hybrid model architecture (Squeeze-ICNN), with a Comb SigHyper-Hsine activation function in the classifier. The model provided experimental results proving accuracy (95.6%), precision (97.9%), sensitivity (94.9%), specificity (92.2%), and lower false negative rate (FNR) (5.1%) than conventional models (e.g., convolutional neural network CNN, deep neural network DNN, LSTM, DenseNet). The study clearly proves the effectiveness and performance of the model to detect CVD from ECG images.
Shilpa et al. (2026) studied Cardiovascular disease (n=928). Squeeze-ICNN model vs. Conventional models (CNN, DNN) was evaluated on Classification accuracy. The Squeeze-ICNN deep learning model achieved 95.6% accuracy, 97.9% precision, and 94.9% sensitivity in detecting cardiovascular disease from ECG images.