Dr. Mohd Hanafi Ahmad Hijazi, Associate ProfessorFaculty of Computing and Informatics, Universiti Malaysia Sabah, Malaysia
Speech Title: Deep learning for lung disease detection trends and potential future work
Abstract: The recent developments of deep learning support the identification and classification of lung diseases in medical images. Hence, numerous work on the detection of lung disease using deep learning can be found in the literature, in particular in the last couple of years due to the Covid-19 pandemic. This work presents a survey of work on the application of deep learning for lung disease detection in medical images. The objectives were to develop a taxonomy and visualize the trends of recent work on the domain, to identify the remaining issues and describes potential future directions in this domain. Recent articles published from 2016 to 2022 were considered. From the survey, seven attributes that are common in the surveyed articles were identified. The attributes are the image types, features, data augmentation, types of deep learning algorithms, transfer learning, the ensemble of classifiers, and types of lung diseases. The presented taxonomy could be used by other researchers to plan their research contributions and activities. The potential future direction suggested could further improve the efficiency and increase the number of deep learning aided lung disease detection applications.
Keywords: deep learning, medical image analysis, lung disease detection
Biography: Mohd Hanafi Ahmad Hijazi is an Associate Professor of Computer Science at the Faculty of Computing and Informatics, Universiti Malaysia Sabah in Malaysia. His research work addresses the challenges in knowledge discovery and data mining to identify patterns for prediction on structured and/ or unstructured data; his particular application domains are medical image analysis and understanding and sentiment analysis on social media data. He has authored/ co-authored more than 50 journals/ book chapters and conference papers, most of which are indexed by Scopus and ISI Web of Science. He also served on the program and organizing committees of numerous national and international conferences. He is the leader of the Data Technologies and Applications research group at the faculty.