Explainable Artificial Intelligence (XAI) for Deep Learning Based Medical Imaging Classification
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 t...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-08-01
|
| Series: | Journal of Imaging |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-433X/9/9/177 |
| _version_ | 1797579393737949184 |
|---|---|
| author | Rawan Ghnemat Sawsan Alodibat Qasem Abu Al-Haija |
| author_facet | Rawan Ghnemat Sawsan Alodibat Qasem Abu Al-Haija |
| author_sort | Rawan Ghnemat |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T22:36:31Z |
| format | Article |
| id | doaj.art-b9b9a1fe085e4246a13d80f0e25ae80d |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-10T22:36:31Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-b9b9a1fe085e4246a13d80f0e25ae80d2023-11-19T11:24:38ZengMDPI AGJournal of Imaging2313-433X2023-08-019917710.3390/jimaging9090177Explainable Artificial Intelligence (XAI) for Deep Learning Based Medical Imaging ClassificationRawan Ghnemat0Sawsan Alodibat1Qasem Abu Al-Haija2Department of Computer Science, Princess Sumaya University for Technology, Amman 11941, JordanDepartment of Computer Science, Princess Sumaya University for Technology, Amman 11941, JordanDepartment of Cybersecurity, Princess Sumaya University for Technology, Amman 11941, JordanRecently, 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.https://www.mdpi.com/2313-433X/9/9/177artificial intelligence (AI)explainable AI (XAI)deep learning (DL)convolutional neural network (CNN)medical imaging analysisclassification |
| spellingShingle | Rawan Ghnemat Sawsan Alodibat Qasem Abu Al-Haija Explainable Artificial Intelligence (XAI) for Deep Learning Based Medical Imaging Classification Journal of Imaging artificial intelligence (AI) explainable AI (XAI) deep learning (DL) convolutional neural network (CNN) medical imaging analysis classification |
| title | Explainable Artificial Intelligence (XAI) for Deep Learning Based Medical Imaging Classification |
| title_full | Explainable Artificial Intelligence (XAI) for Deep Learning Based Medical Imaging Classification |
| title_fullStr | Explainable Artificial Intelligence (XAI) for Deep Learning Based Medical Imaging Classification |
| title_full_unstemmed | Explainable Artificial Intelligence (XAI) for Deep Learning Based Medical Imaging Classification |
| title_short | Explainable Artificial Intelligence (XAI) for Deep Learning Based Medical Imaging Classification |
| title_sort | explainable artificial intelligence xai for deep learning based medical imaging classification |
| topic | artificial intelligence (AI) explainable AI (XAI) deep learning (DL) convolutional neural network (CNN) medical imaging analysis classification |
| url | https://www.mdpi.com/2313-433X/9/9/177 |
| work_keys_str_mv | AT rawanghnemat explainableartificialintelligencexaifordeeplearningbasedmedicalimagingclassification AT sawsanalodibat explainableartificialintelligencexaifordeeplearningbasedmedicalimagingclassification AT qasemabualhaija explainableartificialintelligencexaifordeeplearningbasedmedicalimagingclassification |