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The Global ETD Search service is a free service for researchers
to find electronic theses and dissertations. This service is
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Networked Digital Library of Theses and Dissertations.
Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
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Fundus-DeepNet: Multi-Label Deep Learning Classification System for Enhanced Detection of Multiple Ocular Diseases through Data Fusion of Fundus ImagesAl-Fahdawi, S., Al-Waisy, A.S., Zeebaree, D.Q., Qahwaji, Rami S.R., Natiq, H., Mohammed, M.A., Nedoma, J., Martinek, R., Deveci, M. 29 September 2023 (has links)
Yes / Detecting multiple ocular diseases in fundus images is crucial in ophthalmic diagnosis. This study introduces the Fundus-DeepNet system, an automated multi-label deep learning classification system designed to identify multiple ocular diseases by integrating feature representations from pairs of fundus images (e.g., left and right eyes). The study initiates with a comprehensive image pre-processing procedure, including circular border cropping, image resizing, contrast enhancement, noise removal, and data augmentation. Subsequently, discriminative deep feature representations are extracted using multiple deep learning blocks, namely the High-Resolution Network (HRNet) and Attention Block, which serve as feature descriptors. The SENet Block is then applied to further enhance the quality and robustness of feature representations from a pair of fundus images, ultimately consolidating them into a single feature representation. Finally, a sophisticated classification model, known as a Discriminative Restricted Boltzmann Machine (DRBM), is employed. By incorporating a Softmax layer, this DRBM is adept at generating a probability distribution that specifically identifies eight different ocular diseases. Extensive experiments were conducted on the challenging Ophthalmic Image Analysis-Ocular Disease Intelligent Recognition (OIA-ODIR) dataset, comprising diverse fundus images depicting eight different ocular diseases. The Fundus-DeepNet system demonstrated F1-scores, Kappa scores, AUC, and final scores of 88.56%, 88.92%, 99.76%, and 92.41% in the off-site test set, and 89.13%, 88.98%, 99.86%, and 92.66% in the on-site test set.In summary, the Fundus-DeepNet system exhibits outstanding proficiency in accurately detecting multiple ocular diseases, offering a promising solution for early diagnosis and treatment in ophthalmology. / European Union under the REFRESH – Research Excellence for Region Sustainability and High-tech Industries project number CZ.10.03.01/00/22_003/0000048 via the Operational Program Just Transition. The Ministry of Education, Youth, and Sports of the Czech Republic - Technical University of Ostrava, Czechia under Grants SP2023/039 and SP2023/042.
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An explorative study of the technology transfer coach as a preliminary for the design of a computer aidJönsson, Oscar January 2014 (has links)
The university technology transfer coach has an important role in supporting the commercialization of research results. This thesis has studied the technology transfer coach and their needs in the coaching process. The goal has been to investigate information needs of the technology transfer coach as a preliminary for the design of computer aids.Using a grounded theory approach, we interviewed 17 coaches working in the Swedish technology transfer environment. Extracted quotes from interviews were openly coded and categorized. The analysis show three main problem areas related to the information needs of the technology transfer coach; awareness, communication, and resources. Moreover, 20 features for future computer aids were extracted from the interview data and scenarios and personas where developed to exemplify the future use of computer aids.We conclude that there is a need for computer support in the coaching process. Such systems should aid the coach in; awareness, aiding the coach to focus on meetings; communication, aid the coach to transfer commercialisation knowledge; and resources, aid the coach in accessing and delivering of resources to the coachee. However, it is imperative that the computer aids do not interfere with the coach current process; and that the computer aid is not seen as the sole solution.
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