Touba Torabipour - Department of Computer Engineering and Information, Technology Payame Noor University (PNU), Tehran-Iran
Yousef Jahangirigolshavari - Electronic Engineering- Bushehr , Islamic Azad University, Bushehr- Iran
Safieh Siadat - Assistant Professor, Department of Computer Engineering and Information Technology, Payame Noor University (PNU), Tehran, Iran

The human chest contains vital organs such as the heart, lungs, and other organs. Chest radiology is one of the best and least costly methods to diagnose chest diseases. In this study, proposed a new method to diagnose ۱۴ main diseases of the chest such as (cardiomegaly, emphysema, effusion, hernia, nodule, pneumothorax, atelectasis, pleural - thickening, mass, edema, integration, penetration, fibrosis, pneumonia) using the neural network and deep learning to increase accuracy, sensitivity, and specificity. The proposed method is implemented in the form of a web application and is available as a decision-making system for physicians to diagnose chest diseases.The results of the simulation on the sample dataset showed that the diagnosis of chest diseases was ۹۸.۹۳%, indicating the high efficiency of the new method. Finally, the proposed method was compared with other deep learning architectures such as densenet۱۲۱, vgg۱۶, exception architecture on the same dataset, which showed a ۵% higher accuracy than them.

Deep Learning, convolutional neural network, Chest X-Ray