Early-stage cardiomegaly detection and classification from X-ray images using convolutional neural networks and transfer learning

Abstract

• CNN, Inception, DenseNet-169, and ResNet-50, model was developed for better identification of cardiomegaly disease. • The study utilized advanced image enhancement techniques, including CLAHE and BM3D noise filtering, which significantly improved the quality of the X-ray images and, consequently, the accuracy of the model. • Our models can accurately detect early signs of cardiomegaly, supported by visualization techniques like Grad-CAM, underscores its potential for facilitating earlier interventions, which could lead to improved patient outcomes in clinical practice. • ResNet-50 model achieved reliable and accurate training, validation, and testing accuracy of 100 %, 100 %, and 99.8 %, respectively. Cardiomyopathy is a serious condition that can result in heart failure, sudden cardiac death, malignant arrhythmias, and thromboembolism. It is a significant contributor to morbidity and mortality globally. The initial finding of cardiomegaly on radiological imaging may signal a deterioration of a known heart condition, an unknown heart disease, or a heart complication related to another illness. Further cardiological evaluation is needed to confirm the diagnosis and determine appropriate treatment. A chest radiograph (X-ray) is the main imaging method used to identify cardiomegaly when the heart is enlarged. A prompt and accurate diagnosis is essential to help healthcare providers determine the most appropriate treatment options before the condition worsens. This study aims to utilize convolutional neural networks and transfer learning techniques, specifically Inception, DenseNet-169, and ResNet-50, to classify cardiomegaly from chest X-ray images automatically. The utilization of block-matching and 3D filtering (BM3D) techniques aimed at enhancing image edge retention, decreasing noise, and utilizing contrast limited adaptive histogram equalization (CLAHE) to enhance contrast in low-intensity images. Gradient-weighted Class Activation Mapping (GradCAM) was used to visualize the significant activation regions contributing to the model's decision. After evaluating all the models, the ResNet-50 model showed outstanding performance. It achieved perfect accuracy of 100 % in both training, and validation, and an impressive 99.8 % accuracy in testing. Additionally, it displayed complete 100 % precision, recall, and F1-score. These findings demonstrate that ResNet-50 surpassed all other models in the study. As a result, the impressive performance of the ResNet-50 model suggests that it could be a valuable tool in improving the efficiency and accuracy of cardiomyopathy diagnosis, ultimately leading to better patient outcomes.