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Full-Text Articles in Computer Engineering
Detection Of Various Dental Conditions On Dental Panoramic Radiography Using Faster R-Cnn, Shih Lun Chen, Tsung Yi Chen, Yi Cheng Mao, Szu Yin Lin, Ya Yun Huang, Chiung An Chen, Yuan Jin Lin, Mian Heng Chuang, Patricia Angela R. Abu
Detection Of Various Dental Conditions On Dental Panoramic Radiography Using Faster R-Cnn, Shih Lun Chen, Tsung Yi Chen, Yi Cheng Mao, Szu Yin Lin, Ya Yun Huang, Chiung An Chen, Yuan Jin Lin, Mian Heng Chuang, Patricia Angela R. Abu
Department of Information Systems & Computer Science Faculty Publications
The dental panoramic radiograph (DPR) is a pivotal diagnostic tool in dentistry. However, despite the growing prevalence of artificial intelligence (AI) across various medical domains, manual methods remain the prevailing means of interpreting DPR images. This study aims to introduce an advanced identification system for detecting seven dental conditions in DPR images by utilizing Faster R-CNN. The primary objectives are to enhance dentists' efficiency and evaluate the performance of various CNN models as foundational training networks. This study contributes significantly to the field in several notable ways. Firstly, including a Butterworth filter in the training process yielded an approximately 7% …
Analyzing The Benthic Cover Of Crustose Coralline Algae Using Mask-R Cnn, Rachana Ravindra
Analyzing The Benthic Cover Of Crustose Coralline Algae Using Mask-R Cnn, Rachana Ravindra
Master's Projects
Coral reefs, supporting 25% of marine biodiversity, confront challenges from local and global impacts like overfishing, runoff, acidification, and warming. Crustose Coralline Algae (CCA), pivotal for reef structure and coral settlement, are underrepresented in research. Current methods like Coral Point Count with Excel Extensions (CPCe) have limitations, relying on image quality and being time-consuming. This paper proposes computer vision and Mask R-CNN, a supervised machine learning model, for CCA analysis in reef images, considering color, texture, and shape. Results indicate promise in clustering and classifying organisms. The innovative technology reduces manual labor, enhancing image analysis, simplifying the understanding of CCA’s …