Abstract
<span>Recently, cardiovascular diseases (CVDs) have become a rapidly growing problem in the world, especially in developing countries. The latter are facing a lifestyle change that introduces new risk factors for heart disease, that requires a particular and urgent interest. </span><span>Besides, cardiomegaly is a sign of c</span><span>ardiovascular diseases </span><span>that refers to various conditions; it is associated with </span><span>the heart enlargement that can be either transient or permanent depending on certain conditions.</span><span>Furthermore, cardiomegaly is visible on any imaging test including Chest X-Radiation (X-Ray) images; which are one of the most common tools used by Cardiologists to detect and diagnose many diseases</span><span>.</span><span> In this paper, we propose an innovative deep learning (DL) model based on an attention module and MobileNet architecture to recognize Cardiomegaly patients using the popular Chest X-Ray8 dataset. Actually, the attention module captures the spatial relationship between the relevant regions in Chest X-Ray images. The experimental</span><span> results show that the proposed model achieved interesting results with an accuracy rate of 81% which makes it suitable for detecting cardiomegaly disease.</span>
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