Saudi Cultural Missions Theses & Dissertations
Permanent URI for this communityhttps://drepo.sdl.edu.sa/handle/20.500.14154/10
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Item Restricted Exploring Advanced Deep Learning, foundation and Hybrid models for Medical Image Classification(University of Surrey, 2024-09) Kutbi, Jad; Carneiro, GustavoThis dissertation explores the use of advanced deep learning architectures, foundation models, and hybrid models for medical image classification. Medical imaging plays a critical role in the healthcare industry, and deep learning models have demonstrated significant potential in improving the accuracy and efficiency of diagnostic processes. This work focuses on three datasets: RetinaMNIST, BreastMNIST, and FractureMNIST3D from the MedMNISTv2 datasets, each representing different imaging modalities and classification tasks. The significance of this work lies in its comprehensive evaluation of state-of-the-art models, including ResNet, Vision Transformers (ViT), ConvNeXt, and Swin Transformers, and their effectiveness in handling complex medical images. The primary contributions of this research are the implementation and benchmarking of modern architectures on these datasets, as well as the investigation of hyperparameter optimization using Optuna. Pretrained models and hybrid architectures such as CNN-ViT, SwinConvNeXt and CNN-LSTM were explored to enhance performance. Key results demonstrate that models like ConvNeXt-tiny (pretrained) and CLIP achieved high accuracy and AUC scores, particularly in BreastMNIST and RetinaMNIST datasets, setting new performance benchmarks. The combination of Swin and ConvNeXt using feature fusion was shown to improve model robustness, especially when handling multi-class and 3D data.15 0Item Restricted Enhancing Clarity and Readability in Scientific Writing: An Automated Approach to Identifying Shapeless Sentences(Saudi Digital Library, 2023-11-02) Kamal, Ayah; Lopez, AdamEffective communication is essential in academic writing, where clear and coherent writing ensures research findings are disseminated effectively. However, conveying complex concepts in a readable manner remains a challenge in scientific writing. This thesis investigates automating the application of principles from the book Style: Lessons in Clarity and Grace by Williams [32] to improve the readability of scientific writing. The research focuses on identifying “shapeless” sentences that lack structure and clarity. A dataset of scientific sentences sourced from the Elsevier OA Corpus was manually annotated as “Structured”, “Shapeless” or “N/A” based on principles from Style. A Large Language Model, LLaMA-2, was fine-tuned on this dataset to classify the sentences. Optimization techniques like QLoRA enabled efficient fine-tuning within resource constraints. While, prompt engineering and few-shot learning were used to optimize inference. The fine-tuned model achieved promising accuracy in distinguishing between “Structured” and “Shapeless” sentences. The research demonstrates potential for using fine-tuned language models to automate the application of stylistic principles and enhance scientific writing. Further work is needed to expand the dataset, refine definitions, and optimize model training. Overall, this thesis establishes a foundation for using language models to identify problematic sentences and improve readability14 0