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Explainable Artificial Intelligence (XAI) for Deep Learning Based Medical Imaging Classification by Rawan Ghnemat; Sawsan Alodibat; Qasem Abu Al-Haija is a Computer Science article available to read on EtoBox.

What is Explainable Artificial Intelligence (XAI) for Deep Learning Based Medical Imaging Classification about?

Recently, deep learning has gained significant attention as a noteworthy division of artificial intelligence (AI) due to its high accuracy and versatile applications. However, one of the major challenges of AI is the need for more interpretability, commonly referred to as the black-box problem. In this study, we introduce an explainable AI model for medical image classification to enhance the interpretability of the decision-making process. Our approach is based on segmenting the images to provide a better understanding of how the AI model arrives at its results. We evaluated our model on five datasets, including the COVID-19 and Pneumonia Chest X-ray dataset, Chest X-ray (COVID-19 and Pneumonia), COVID-19 Image Dataset (COVID-19, Viral Pneumonia, Normal), and COVID-19 Radiography Database. We achieved testing and validation accuracy of 90.6% on a relatively small dataset of 6432 images. Our proposed model improved accuracy and reduced time complexity, making it more practical for medical diagnosis. Our approach offers a more interpretable and transparent AI model that can enhance the accuracy and efficiency of medical diagnosis.

Who reads Explainable Artificial Intelligence (XAI) for Deep Learning Based Medical Imaging Classification?

Author

Rawan Ghnemat; Sawsan Alodibat; Qasem Abu Al-Haija

Publisher

MDPI AG

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)