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Recent Submissions

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Comparing Vision Transformers and CNNs for Accurate Retinal Disease Classification
(2025) Paudyal, Binod; Asst. Prof. Sarbin Sayami
Retinal diseases, such as Age-Related Macular Degeneration (AMD) and Diabetic Macular Edema (DME) significanty contribute to vision impairment in global scale. An early diagnosis and timely treatment can save a lot of people form blindness. This research focuses on leveraging Optical Coherence Tomography (OCT) images for the classification of retinal diseases using advanced deep learning models. Specifically, we explore the capabilities of Vision Transformers (ViTs), Convolutional Neural Networks (CNNs), and a proposed Hybrid CNN-Transformer model (HybridCNNViT). The HybridCNNViT model was developed by combining the local feature extraction strengths of CNNs with the global context modeling capabilities of Transformers. Comparative evaluations of accuracy, precision, and computational efficiency revealed that HybridCNNViT outperforms standalone ViTs and CNNs for retinal disease classification. As it offers a promising approach to improve healthcare outcomes in ophthalmology, can be further improved and used in applications of automated retinal disease detection and clinical diagnostics.
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Comparative Analysis of ResNet50 and Vision Transformer on Paddy Disease Classification
(2025) Bhattarai, Bhawana; Asst. Prof. Bikash Balami
Plant diseases seriously affect our food supply, thereby affecting farmers, those dependent on farming, and global food security. Early detection of plant disease is critical for effective treatment and minimized yield losses. One of the useful uses of computer vision is the identification of plant diseases by analyzing leaf images. This study compares the ResNet50 model and the ViT model on the paddy disease classification task. Specifically, it trains these models using the Paddy Doctor Dataset to evaluate their performance by modifying the learning rate and the number of training epochs. The Paddy Doctor dataset, which contains 16,225 images categorized into 13 different classes, was used to train and test the models. The ResNet50 model achieved a high training accuracy of 0.98. However, when evaluated on the test dataset, the model's performance decreased, achieving an accuracy of 0.92. On the other hand, the ViT model achieved a remarkably high training accuracy of 0.99. When evaluated on the test dataset, the ViT model maintained strong performance, with an accuracy of 0.93. These results indicate that the ResNet50 model outperforms the ViT model in terms of both training and test accuracy for the paddy disease classification task using the Paddy Doctor dataset.
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Voltage Impact Analysis for VSC-Based DGs Connected to the Primary Distribution System
(I.O.E, 2025-04) Bhattarai, Upendra; Adhikari, Sujan