Summary of A Novel Ensemble-based Deep Learning Model with Explainable Ai For Accurate Kidney Disease Diagnosis, by Md. Arifuzzaman et al.
A Novel Ensemble-Based Deep Learning Model with Explainable AI for Accurate Kidney Disease Diagnosis
by Md. Arifuzzaman, Iftekhar Ahmed, Md. Jalal Uddin Chowdhury, Shadman Sakib, Mohammad Shoaib Rahman, Md. Ebrahim Hossain, Shakib Absar
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents an innovative application of transfer learning models to diagnose Chronic Kidney Disease (CKD) efficiently. The study utilizes a comprehensive publicly available dataset and evaluates the performance of four state-of-the-art models: EfficientNetV2, InceptionNetV2, MobileNetV2, and Vision Transformer (ViT). Notably, MobileNetV2 achieves 90% accuracy, while ViT reaches 91.5%. To further enhance predictive capabilities, an ensemble model is developed, resulting in a remarkable 96% accuracy for early CKD detection. This breakthrough has immense promise to improve clinical outcomes, highlighting the crucial role of machine learning in addressing complex medical challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at using special computer models to help doctors diagnose a serious health problem called Chronic Kidney Disease (CKD) earlier and more accurately. The researchers tested different models on a large dataset and found that one model, MobileNetV2, worked particularly well, correctly diagnosing over 90% of cases. They also combined several models together for even better results. This could lead to better treatment options and improved health outcomes. |
Keywords
» Artificial intelligence » Ensemble model » Machine learning » Transfer learning » Vision transformer » Vit
