Explainable Ai Based Glaucoma Detection Using Transfer Learning And

Explainable Ai Based Glaucoma Detection Using Transfer Learning And In this article after comparing with various pre trained models, we propose a transfer learning model that is able to classify glaucoma with 94.71% accuracy. in addition, we have utilized local interpretable model agnostic explanations (lime) that introduces explainability in our system. In this research, we have a proposed a model for detecting glaucoma diseases based on transfer learning along with a thorough comparison of the effectiveness of several pre trained models for the classification of glaucoma.

Explainable Ai Based Glaucoma Detection Using Transfer Learning And Explainable ai based glaucoma detection using transfer learning, lime and llms. this project presents a multi modal framework for glaucoma classification, combining transfer learning, explainable ai (xai), and advanced large language models (llms). In this article after comparing with various pre trained models, we propose a transfer learning model that is able to classify glaucoma with 94.71\% accuracy. in addition, we have utilized. This systematic review examines how explainable artificial intelligence (xai) enhances the transparency and comprehensibility of machine learning algorithms for glaucoma detection. This paper proposes an explainable artificial intelligence (xai) based model for automatic glaucoma detection using pre‐trained convolutional neural networks (pcnns) and machine learning classifiers (mlcs).

Explainable Ai Based Glaucoma Detection Using Transfer Learning And This systematic review examines how explainable artificial intelligence (xai) enhances the transparency and comprehensibility of machine learning algorithms for glaucoma detection. This paper proposes an explainable artificial intelligence (xai) based model for automatic glaucoma detection using pre‐trained convolutional neural networks (pcnns) and machine learning classifiers (mlcs). Similar content being viewed by others oct based diagnosis of glaucoma and glaucoma stages using explainable machine learning article open access 28 january 2025. Glaucoma is a leading cause of irreversible blindness worldwide, yet early detection can prevent vision loss. this paper proposes a novel deep learning approach that combines two ophthalmic imaging modalities, fundus photographs and optical coherence tomography scans, as paired images from the same eye of each patient for automated glaucoma detection. we develop separate convolutional neural. In this article after comparing with various pre trained models, we propose a transfer learning model that is able to classify glaucoma with 94.71\% accuracy. in addition, we have utilized local interpretable model agnostic explanations (lime) that introduces explainability in our system. This project leverages explainable ai for glaucoma detection using transfer learning and lime. it features a react.js frontend for user interaction, node.js with express.js for backend apis, fastapi for model serving, and mongodb for data storage.

Explainable Ai Based Glaucoma Detection Using Transfer Learning And Similar content being viewed by others oct based diagnosis of glaucoma and glaucoma stages using explainable machine learning article open access 28 january 2025. Glaucoma is a leading cause of irreversible blindness worldwide, yet early detection can prevent vision loss. this paper proposes a novel deep learning approach that combines two ophthalmic imaging modalities, fundus photographs and optical coherence tomography scans, as paired images from the same eye of each patient for automated glaucoma detection. we develop separate convolutional neural. In this article after comparing with various pre trained models, we propose a transfer learning model that is able to classify glaucoma with 94.71\% accuracy. in addition, we have utilized local interpretable model agnostic explanations (lime) that introduces explainability in our system. This project leverages explainable ai for glaucoma detection using transfer learning and lime. it features a react.js frontend for user interaction, node.js with express.js for backend apis, fastapi for model serving, and mongodb for data storage.