Effectiveness of Learning Systems from Common Image File Types to Detect Osteosarcoma Based on Convolutional Neural Networks (CNNs) Models
Osteosarcoma is a rare bone cancer which is more common in children than in adults and has a high chance of metastasizing to the patient’s lungs. Due to initiated cases, it is difficult to diagnose and hard to detect the nodule in a lung at the early state. Convolutional Neural Networks (CNNs) are e...
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| Format: | Article |
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MDPI AG
2021-12-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/8/1/2 |
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| author | Chanunya Loraksa Sirima Mongkolsomlit Nitikarn Nimsuk Meenut Uscharapong Piya Kiatisevi |
| author_facet | Chanunya Loraksa Sirima Mongkolsomlit Nitikarn Nimsuk Meenut Uscharapong Piya Kiatisevi |
| author_sort | Chanunya Loraksa |
| collection | DOAJ |
| description | Osteosarcoma is a rare bone cancer which is more common in children than in adults and has a high chance of metastasizing to the patient’s lungs. Due to initiated cases, it is difficult to diagnose and hard to detect the nodule in a lung at the early state. Convolutional Neural Networks (CNNs) are effectively applied for early state detection by considering CT-scanned images. Transferring patients from small hospitals to the cancer specialized hospital, Lerdsin Hospital, poses difficulties in information sharing because of the privacy and safety regulations. CD-ROM media was allowed for transferring patients’ data to Lerdsin Hospital. Digital Imaging and Communications in Medicine (DICOM) files cannot be stored on a CD-ROM. DICOM must be converted into other common image formats, such as BMP, JPG and PNG formats. Quality of images can affect the accuracy of the CNN models. In this research, the effect of different image formats is studied and experimented. Three popular medical CNN models, VGG-16, ResNet-50 and MobileNet-V2, are considered and used for osteosarcoma detection. The positive and negative class images are corrected from Lerdsin Hospital, and 80% of all images are used as a training dataset, while the rest are used to validate the trained models. Limited training images are simulated by reducing images in the training dataset. Each model is trained and validated by three different image formats, resulting in 54 testing cases. F1-Score and accuracy are calculated and compared for the models’ performance. VGG-16 is the most robust of all the formats. PNG format is the most preferred image format, followed by BMP and JPG formats, respectively. |
| first_indexed | 2024-03-10T01:12:44Z |
| format | Article |
| id | doaj.art-eef0e9d8a9964d4fab3d49dccf43e866 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-10T01:12:44Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-eef0e9d8a9964d4fab3d49dccf43e8662023-11-23T14:15:08ZengMDPI AGJournal of Imaging2313-433X2021-12-0181210.3390/jimaging8010002Effectiveness of Learning Systems from Common Image File Types to Detect Osteosarcoma Based on Convolutional Neural Networks (CNNs) ModelsChanunya Loraksa0Sirima Mongkolsomlit1Nitikarn Nimsuk2Meenut Uscharapong3Piya Kiatisevi4Medical Engineering, Faculty of Engineering, Thammasat University, Pathum Thani 12121, ThailandFaculty of Public Health, Thammasat University, Pathum Thani 12121, ThailandMedical Engineering, Faculty of Engineering, Thammasat University, Pathum Thani 12121, ThailandDepartment of Medical Services, Lerdsin Hospital, Ministry of Public Health in Thailand, Bangkok 10500, ThailandDepartment of Medical Services, Lerdsin Hospital, Ministry of Public Health in Thailand, Bangkok 10500, ThailandOsteosarcoma is a rare bone cancer which is more common in children than in adults and has a high chance of metastasizing to the patient’s lungs. Due to initiated cases, it is difficult to diagnose and hard to detect the nodule in a lung at the early state. Convolutional Neural Networks (CNNs) are effectively applied for early state detection by considering CT-scanned images. Transferring patients from small hospitals to the cancer specialized hospital, Lerdsin Hospital, poses difficulties in information sharing because of the privacy and safety regulations. CD-ROM media was allowed for transferring patients’ data to Lerdsin Hospital. Digital Imaging and Communications in Medicine (DICOM) files cannot be stored on a CD-ROM. DICOM must be converted into other common image formats, such as BMP, JPG and PNG formats. Quality of images can affect the accuracy of the CNN models. In this research, the effect of different image formats is studied and experimented. Three popular medical CNN models, VGG-16, ResNet-50 and MobileNet-V2, are considered and used for osteosarcoma detection. The positive and negative class images are corrected from Lerdsin Hospital, and 80% of all images are used as a training dataset, while the rest are used to validate the trained models. Limited training images are simulated by reducing images in the training dataset. Each model is trained and validated by three different image formats, resulting in 54 testing cases. F1-Score and accuracy are calculated and compared for the models’ performance. VGG-16 is the most robust of all the formats. PNG format is the most preferred image format, followed by BMP and JPG formats, respectively.https://www.mdpi.com/2313-433X/8/1/2osteosarcomabone cancerConvolutional Neural Networkscommon image filecomputer-aided diagnosis |
| spellingShingle | Chanunya Loraksa Sirima Mongkolsomlit Nitikarn Nimsuk Meenut Uscharapong Piya Kiatisevi Effectiveness of Learning Systems from Common Image File Types to Detect Osteosarcoma Based on Convolutional Neural Networks (CNNs) Models Journal of Imaging osteosarcoma bone cancer Convolutional Neural Networks common image file computer-aided diagnosis |
| title | Effectiveness of Learning Systems from Common Image File Types to Detect Osteosarcoma Based on Convolutional Neural Networks (CNNs) Models |
| title_full | Effectiveness of Learning Systems from Common Image File Types to Detect Osteosarcoma Based on Convolutional Neural Networks (CNNs) Models |
| title_fullStr | Effectiveness of Learning Systems from Common Image File Types to Detect Osteosarcoma Based on Convolutional Neural Networks (CNNs) Models |
| title_full_unstemmed | Effectiveness of Learning Systems from Common Image File Types to Detect Osteosarcoma Based on Convolutional Neural Networks (CNNs) Models |
| title_short | Effectiveness of Learning Systems from Common Image File Types to Detect Osteosarcoma Based on Convolutional Neural Networks (CNNs) Models |
| title_sort | effectiveness of learning systems from common image file types to detect osteosarcoma based on convolutional neural networks cnns models |
| topic | osteosarcoma bone cancer Convolutional Neural Networks common image file computer-aided diagnosis |
| url | https://www.mdpi.com/2313-433X/8/1/2 |
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