Diagnosis Support Model of Cardiomegaly based on CNN using ResNet and Explainable Feature Map

This study presents a Diagnosis Support Model for Cardiomegaly using Convolutional Neural Networks (CNNs) with a focus on the Residual Network (ResNet) architecture, enhanced by Explainable Feature Maps. Cardiomegaly is a critical cardiac condition characterized by an enlarged heart, and early detection is vital for timely intervention. This research aims to leverage deep learning techniques to automatically classify X-ray images and provide clinicians with an efficient diagnostic tool. By incorporating Explainable Feature Maps, the model enhances interpretability, assisting medical professionals in understanding the key visual cues driving the classification decisions. The proposed model holds promise for accurate and transparent Cardiomegaly diagnosis.