Hamed Behzadi-Khormouji1, Habib Rostami2, Sana Salehi3, Touba Derakhshande-Rishehri1, Marzieh Masoumi1, Siavash Salemi1, Ahmad Keshavarz4, Ali Gholamrezanezhad5, Majid Assadi6, Ali Batouli7. 1. Computer Engineering Department, School of Engineering, Persian Gulf University, Bushehr, Iran. 2. Computer Engineering Department, School of Engineering, Persian Gulf University, Bushehr, Iran. Electronic address: habib@pgu.ac.ir. 3. Department of Radiology, Mercer University School of Medicine, Savannah, GA, USA. 4. Electrical Engineering Department, School of Engineering, Persian Gulf University, Bushehr, Iran. 5. Fellow of the European Board of Nuclear Medicine (FEBNM), USA. 6. The Persian Gulf Nuclear Medicine Research Center, Bushehr University of Medical Science, Bushehr, Iran. 7. Departmentof Radiology, Oregon Health and Science University, 320 East North Ave., Pittsburgh, PA, 15214, USA.
Abstract
BACKGROUND AND OBJECTIVE: In most patients presenting with respiratory symptoms, the findings of chest radiography play a key role in the diagnosis, management, and follow-up of the disease. Consolidation is a common term in radiology, which indicates focally increased lung density. When the alveolar structures become filled with pus, fluid, blood cells or protein subsequent to a pulmonary pathological process, it may result in different types of lung opacity in chest radiograph. This study aims at detecting consolidations in chest x-ray radiographs, with a certain precision, using artificial intelligence and especially Deep Convolutional Neural Networks to assist radiologist for better diagnosis. METHODS: Medical image datasets usually are relatively small to be used for training a Deep Convolutional Neural Network (DCNN), so transfer learning technique with well-known DCNNs pre-trained with ImageNet dataset are used to improve the accuracy of the models. ImageNet feature space is different from medical images and in the other side, the well-known DCNNs are designed to achieve the best performance on ImageNet. Therefore, they cannot show their best performance on medical images. To overcome this problem, we designed a problem-based architecture which preserves the information of images for detecting consolidation in Pediatric Chest X-ray dataset. We proposed a three-step pre-processing approach to enhance generalization of the models. To demonstrate the correctness of numerical results, an occlusion test is applied to visualize outputs of the model and localize the detected appropriate area. A different dataset as an extra validation is used in order to investigate the generalization of the proposed model. RESULTS: The best accuracy to detect consolidation is 94.67% obtained by our problem based architecture for the understudy dataset which outperforms the previous works and the other architectures. CONCLUSIONS: The designed models can be employed as computer aided diagnosis tools in real practice. We critically discussed the datasets and the previous works based on them and show that without some considerations the results of them may be misleading. We believe, the output of AI should be only interpreted as focal consolidation. The clinical significance of the finding can not be interpreted without integration of clinical data.
BACKGROUND AND OBJECTIVE: In most patients presenting with respiratory symptoms, the findings of chest radiography play a key role in the diagnosis, management, and follow-up of the disease. Consolidation is a common term in radiology, which indicates focally increased lung density. When the alveolar structures become filled with pus, fluid, blood cells or protein subsequent to a pulmonary pathological process, it may result in different types of lung opacity in chest radiograph. This study aims at detecting consolidations in chest x-ray radiographs, with a certain precision, using artificial intelligence and especially Deep Convolutional Neural Networks to assist radiologist for better diagnosis. METHODS: Medical image datasets usually are relatively small to be used for training a Deep Convolutional Neural Network (DCNN), so transfer learning technique with well-known DCNNs pre-trained with ImageNet dataset are used to improve the accuracy of the models. ImageNet feature space is different from medical images and in the other side, the well-known DCNNs are designed to achieve the best performance on ImageNet. Therefore, they cannot show their best performance on medical images. To overcome this problem, we designed a problem-based architecture which preserves the information of images for detecting consolidation in Pediatric Chest X-ray dataset. We proposed a three-step pre-processing approach to enhance generalization of the models. To demonstrate the correctness of numerical results, an occlusion test is applied to visualize outputs of the model and localize the detected appropriate area. A different dataset as an extra validation is used in order to investigate the generalization of the proposed model. RESULTS: The best accuracy to detect consolidation is 94.67% obtained by our problem based architecture for the understudy dataset which outperforms the previous works and the other architectures. CONCLUSIONS: The designed models can be employed as computer aided diagnosis tools in real practice. We critically discussed the datasets and the previous works based on them and show that without some considerations the results of them may be misleading. We believe, the output of AI should be only interpreted as focal consolidation. The clinical significance of the finding can not be interpreted without integration of clinical data.
Authors: Nazish Sayed; Yingxiang Huang; Khiem Nguyen; Zuzana Krejciova-Rajaniemi; Anissa P Grawe; Tianxiang Gao; Robert Tibshirani; Trevor Hastie; Ayelet Alpert; Lu Cui; Tatiana Kuznetsova; Yael Rosenberg-Hasson; Rita Ostan; Daniela Monti; Benoit Lehallier; Shai S Shen-Orr; Holden T Maecker; Cornelia L Dekker; Tony Wyss-Coray; Claudio Franceschi; Vladimir Jojic; François Haddad; José G Montoya; Joseph C Wu; Mark M Davis; David Furman Journal: Nat Aging Date: 2021-07-12
Authors: Narathip Reamaroon; Michael W Sjoding; Harm Derksen; Elyas Sabeti; Jonathan Gryak; Ryan P Barbaro; Brian D Athey; Kayvan Najarian Journal: BMC Med Imaging Date: 2020-10-15 Impact factor: 1.930