A Deep Learning Approach for Diagnosis Chest Diseases

Document Type : Original Article


1 Department of Computer Engineering and Information, Technology Payame Noor University (PNU), Tehran-Iran

2 Electronic Engineering- Bushehr , Islamic Azad University, Bushehr- Iran

3 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 14 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 98.93%, indicating the high efficiency of the new method. Finally, the proposed method was compared with other deep learning architectures such as densenet121, vgg16, exception architecture on the same dataset, which showed a 5% higher accuracy than them.


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Touba Torabipour has a bachelor's degree, master's degree in computer engineering, majoring in software engineering. Her areas of interest include Deep learning, reinforcement learning, machine vision, artificial intelligence, and the Internet of Things. She has published more than 4 articles in prestigious Iranian and international journals and conferences.

Yousef Jahangiri Golshvari has a bachelor's degree in electrical engineering with a focus on electronic engineering from Bushehr Azad University. He has been active in the field of industry-university communication since 1386 in Wireless, design and implementation of urban fire alarm network, fire forecasting systems, process automation of oil, steel and petrochemical industries, the use of artificial intelligence in the prediction of plant diseases and smart plant.

 Seyedeh Safieh Siadat, Assistant Professor, Payame Noor University, has a bachelor's, master's and doctoral degree in computer engineering majoring in software engineering. She has been a full-time faculty member at Payame Noor University since 1996. His areas of interest include the Internet of Things, game theory, machine learning, and cloud computing. She has published more than 40 articles in prestigious international journals and conferences.