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

Overview

Journal J Imaging

Publisher MDPI

Specialty Radiology

Date 2023 Sep 27

PMID 37754941

Authors

Rawan Ghnemat

Sawsan Alodibat

Qasem Abu Al-Haija

Affiliations

Soon will be listed here.

Abstract

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.

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References

Li H, Chen X, Qian X, Chen H, Li Z, Bhattacharjee S . An explainable COVID-19 detection system based on human sounds. Smart Health (Amst). 2022; 26:100332. PMC: 9580234. DOI: 10.1016/j.smhl.2022.100332. View

Qi X, Brown L, Foran D, Nosher J, Hacihaliloglu I . Chest X-ray image phase features for improved diagnosis of COVID-19 using convolutional neural network. Int J Comput Assist Radiol Surg. 2021; 16(2):197-206. PMC: 7794081. DOI: 10.1007/s11548-020-02305-w. View

Alqahtani A, Zahoor M, Nasrullah R, Fareed A, Cheema A, Shahrose A . Computer Aided COVID-19 Diagnosis in Pandemic Era Using CNN in Chest X-ray Images. Life (Basel). 2022; 12(11). PMC: 9697164. DOI: 10.3390/life12111709. View

Linardatos P, Papastefanopoulos V, Kotsiantis S . Explainable AI: A Review of Machine Learning Interpretability Methods. Entropy (Basel). 2020; 23(1). PMC: 7824368. DOI: 10.3390/e23010018. View

Zhong A, Li X, Wu D, Ren H, Kim K, Kim Y . Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19. Med Image Anal. 2021; 70:101993. PMC: 8032481. DOI: 10.1016/j.media.2021.101993. View

Karaci A . VGGCOV19-NET: automatic detection of COVID-19 cases from X-ray images using modified VGG19 CNN architecture and YOLO algorithm. Neural Comput Appl. 2022; 34(10):8253-8274. PMC: 8785935. DOI: 10.1007/s00521-022-06918-x. View

Abbas A, Abdelsamea M, Gaber M . Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. Appl Intell (Dordr). 2021; 51(2):854-864. PMC: 7474514. DOI: 10.1007/s10489-020-01829-7. View

Wu Y, Gao S, Mei J, Xu J, Fan D, Zhang R . JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation. IEEE Trans Image Process. 2021; 30:3113-3126. DOI: 10.1109/TIP.2021.3058783. View

Dong S, Yang Q, Fu Y, Tian M, Zhuo C . RCoNet: Deformable Mutual Information Maximization and High-Order Uncertainty-Aware Learning for Robust COVID-19 Detection. IEEE Trans Neural Netw Learn Syst. 2021; 32(8):3401-3411. PMC: 8864918. DOI: 10.1109/TNNLS.2021.3086570. View

10.

Singh R, Pandey R, Babu R . COVIDScreen: explainable deep learning framework for differential diagnosis of COVID-19 using chest X-rays. Neural Comput Appl. 2021; 33(14):8871-8892. PMC: 7791540. DOI: 10.1007/s00521-020-05636-6. View

11.

Mondal A, Bhattacharjee A, Singla P, Prathosh A . xViTCOS: Explainable Vision Transformer Based COVID-19 Screening Using Radiography. IEEE J Transl Eng Health Med. 2021; 10:1100110. PMC: 8691725. DOI: 10.1109/JTEHM.2021.3134096. View

12.

Lucas B, Vahedi B, Karimzadeh M . A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA. Int J Data Sci Anal. 2022; 15(3):247-266. PMC: 8760128. DOI: 10.1007/s41060-021-00295-9. View

13.

Hou J, Gao T . Explainable DCNN based chest X-ray image analysis and classification for COVID-19 pneumonia detection. Sci Rep. 2021; 11(1):16071. PMC: 8352869. DOI: 10.1038/s41598-021-95680-6. View

14.

Motamed S, Rogalla P, Khalvati F . RANDGAN: Randomized generative adversarial network for detection of COVID-19 in chest X-ray. Sci Rep. 2021; 11(1):8602. PMC: 8060427. DOI: 10.1038/s41598-021-87994-2. View

15.

Mousavi Z, Shahini N, Sheykhivand S, Mojtahedi S, Arshadi A . COVID-19 detection using chest X-ray images based on a developed deep neural network. SLAS Technol. 2022; 27(1):63-75. PMC: 8545610. DOI: 10.1016/j.slast.2021.10.011. View

16.

Arias-Londono J, Gomez-Garcia J, Moro-Velazquez L, Godino-Llorente J . Artificial Intelligence Applied to Chest X-Ray Images for the Automatic Detection of COVID-19. A Thoughtful Evaluation Approach. IEEE Access. 2021; 8:226811-226827. PMC: 8545248. DOI: 10.1109/ACCESS.2020.3044858. View

17.

Wang Y, Jiang C, Wu Y, Lv T, Sun H, Liu Y . Semantic-Powered Explainable Model-Free Few-Shot Learning Scheme of Diagnosing COVID-19 on Chest X-Ray. IEEE J Biomed Health Inform. 2022; 26(12):5870-5882. DOI: 10.1109/JBHI.2022.3205167. View

18.

Safieh A, Alhaol I, Ghnemat R . End-to-end Jordanian dialect speech-to-text self-supervised learning framework. Front Robot AI. 2023; 9:1090012. PMC: 9815896. DOI: 10.3389/frobt.2022.1090012. View

19.

Wang L, Lin Z, Wong A . COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Sci Rep. 2020; 10(1):19549. PMC: 7658227. DOI: 10.1038/s41598-020-76550-z. View

20.

Shahid O, Nasajpour M, Pouriyeh S, Parizi R, Han M, Valero M . Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance. J Biomed Inform. 2021; 117:103751. PMC: 7987503. DOI: 10.1016/j.jbi.2021.103751. View