AUNET- An Attention based Deep Learning Model for Detecting Pulmonary Embolism

Pulmonary Embolism (PE) demands timely, accurate diagnosis to save lives. Reducing mortality rates from pulmonary embolism (PE), a lethal disease, needs early and precise diagnosis. Conventional diagnostic techniques, despite their accuracy, are too time-consuming and prone to human error. This paper proposes AUNet, an advanced deep learning model specifically for detecting PE in computed tomography (CT) scan images. AUNet provides a robust and reliable solution for PE detection, supporting clinical decision-making and improving patient outcomes.