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Found 5 papers, 5 papers with code
Explainable Convolutional Neural Networks for Retinal Fundus Classification and Cutting-Edge Segmentation Models for Retinal Blood Vessels from Fundus Images
Our research focuses on the critical field of early diagnosis of disease by examining retinal blood vessels in fundus images.
PotatoGANs: Utilizing Generative Adversarial Networks, Instance Segmentation, and Explainable AI for Enhanced Potato Disease Identification and Classification
Using the Inception score as a measure, our experiments show the better quality and realisticness of the images created by PotatoGANs, emphasizing their capacity to resemble real disease images closely.
Exploring Explainable AI Techniques for Improved Interpretability in Lung and Colon Cancer Classification
Lung and colon cancer are serious worldwide health challenges that require early and precise identification to reduce mortality risks.
Unraveling the Dominance of Large Language Models Over Transformer Models for Bangla Natural Language Inference: A Comprehensive Study
Through a comprehensive evaluation, we assess the performance of prominent LLMs and state-of-the-art (SOTA) models in Bengali NLP tasks, focusing on natural language inference.
Vashantor: A Large-scale Multilingual Benchmark Dataset for Automated Translation of Bangla Regional Dialects to Bangla Language
This is the first large-scale investigation of Bangla regional dialects to Bangla machine translation.
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