Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
In this research, we dive into a comparative study of Explainable Artificial Intelligence (XAI) models used in medical image diagnosis. While AI models, particularly deep learning, have shown impressive results in spotting abnormalities in medical images, their lack of transparency can be a real concern when it comes to clinical decision-making. This study takes a closer look at various XAI techniques—like LIME, SHAP, Grad-CAM, and Integrated Gradients—across different deep learning models, utilizing medical imaging datasets such as ChestX-ray14 and HAM10000. We assess metrics like accuracy, interpretability, and how usable these models are for clinicians. The findings shed light on the balance between model performance and explainability, providing valuable insights for choosing the best XAI approach in clinical environments.
Keywords:
Medical Image Diagnosis, Deep Learning, Model Interpretability, Grad-CAM, SHAP
Cite Article:
"A Comparative Study on Explainable AI Models for Medical Image Diagnosis", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.c321-c325, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505236.pdf
Downloads:
000297
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator