A Deep Learning Approach for Diagnosis Chest Diseases
Publish place: International Journal of Web Research، Vol: 4، Issue: 1
Publish Year: 1400
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_IJWR-4-1_002
تاریخ نمایه سازی: 11 آبان 1400
Abstract:
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.
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Authors
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