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 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.

Einzug der EDV im österreichischen Bibliothekswesen am Beispiel der Universitätsbibliothek Graz

Durch den Einsatz von EDV-Systemen kam es ab den 1970er Jahren zu einem radikalen Wandel in der Benutzung und Verwaltung von Universitätsbibliotheken. Die Universitätsbibliothek Graz war die erste Bibliothek in Österreich, die ein elektronisches Bibliothekssystem entwickelte und einsetzte, womit sie zu den Vorreitern in Europa zählte. Dieser Artikel liefert einen historischen Überblick über die Anfänge, die Entwicklung und Verbreitung der elektronischen Bibliothekssysteme im Allgemeinen sowie an der Universitätsbibliothek Graz im Speziellen. Vorgestellt werden die im Lauf der Jahrzehnte an der UB Graz eingesetzten Bibliothekssysteme GRIBS, EMILE, FBInfo, BIBOS, ALEPH und ALMA sowie die Entwicklung von den ersten Online- über die CD-ROM-Datenbanken bis hin zum modernen Datenbank-Retrieval.