CXR-based lung disease classification using convolutional neural network / Jannie Fleur V. Oraño
By: Oraño, Jannie Fleur V [author]
Language: English Description: viii, 83 leaves: color illustrations; 28 cmContent type: text Media type: unmediated Carrier type: volumeSubject(s): Lungs -- Diseases | Neural networks (Computer science) | Diagnostic Imaging -- methodsGenre/Form: Academic theses.DDC classification: 616.240285 Dissertation note: Thesis (College of Computer Studies) -- Cebu Institute of Technology University October 2019 Abstract: Lung disease like effusion, pneumothorax, atelectasis and tuberculosis are some of the most severe and prevailing health problems in people’s life. A large percentage of the human population around the world is diagnosed annually with lung disease affecting adults, teens, children, smokers, and even non-smokers making it be considered as the leading cause of death and disability worldwide. Early and accurate diagnosis of chest diseases in mandatory and needed for timely and successful treatment, prevents further complications and a higher likelihood of survival. This study demonstrated the feasibility of classifying lung diseases in chest X-rays using conventional and deep learning approaches. With 8,125 sample images, the neural network model managed to achieve 82.53% accuracy in the classification. This accuracy rate indicates the generated model can significantly automate the differential diagnosis of lung disease using chest radiograph. The developed applications, when used as a second opinion, can help assists radiologist and doctors in their diagnosis and decision making. However, this accuracy can be further improved if more and balanced training images will be utilized.| Item type | Current location | Home library | Call number | Status | Date due | Barcode | Item holds |
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THESIS / DISSERTATION
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GRADUATE LIBRARY | GRADUATE LIBRARY | 616.240285 Or15 2019 (Browse shelf) | Not for loan | CL-T2047 |
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| 616.12 M3934 2023 Mastering structural heart disease / | 616.1207543 D3948 2024 Echocardiography : a practical guide for reporting and interpretation / | 616.1207547 El255 2023 The electrocardiogram in emergency and acute care / | 616.240285 Or15 2019 CXR-based lung disease classification using convolutional neural network / | 616.9 N227 2006 Handbook of common communicable and infectious diseases / | 616.90 N227 201 Handbook of common communicable and infectious diseases / | 616.90 N227 201 Handbook of common communicable and infectious diseases / |
Thesis (College of Computer Studies) -- Cebu Institute of Technology University October 2019
Includes bibliographical references.
Lung disease like effusion, pneumothorax, atelectasis and tuberculosis are some of the most severe and prevailing health problems in people’s life. A large percentage of the human population around the world is diagnosed annually with lung disease affecting adults, teens, children, smokers, and even non-smokers making it be considered as the leading cause of death and disability worldwide. Early and accurate diagnosis of chest diseases in mandatory and needed for timely and successful treatment, prevents further complications and a higher likelihood of survival. This study demonstrated the feasibility of classifying lung diseases in chest X-rays using conventional and deep learning approaches. With 8,125 sample images, the neural network model managed to achieve 82.53% accuracy in the classification. This accuracy rate indicates the generated model can significantly automate the differential diagnosis of lung disease using chest radiograph. The developed applications, when used as a second opinion, can help assists radiologist and doctors in their diagnosis and decision making. However, this accuracy can be further improved if more and balanced training images will be utilized.
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