Abstract
Deep neural networks (DNNs) have shown great potential in diagnosing brain tumor disorder, but their decision-making processes can be difficult to interpret, leading to concerns about their reliability and safety. This paper presents overview of explainable artificial intelligence techniques which have been developed to improve the interpretability and transparency of DNNs and have been applied to diagnostic systems for such disorders. Based on the utilized framework of explainable artificial intelligence (XAI) in collaboration with deep learning models, authors diagnosed brain tumor with the help of convolutional neural network and interpreted its outcomes with the help of numerical gradient-weighted class activation mapping (numGrad-CAM-CNN), therefore achieved highest accuracy of 97.11%. Thus, XAI can help healthcare professionals in understanding how a DNN arrived at a diagnosis, providing insights into the reasoning and decision-making processes of the model. XAI techniques can also help to identify biases in the data used to train the model and address potential ethical concerns. However, challenges remain in implementing XAI techniques in diagnostic systems, including the need for large, diverse datasets, and the development of user-friendly interfaces. Despite these challenges, the potential benefits for improving patient outcomes and increasing trust in AI-based medical systems make it a promising area of research.
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References
Louis DN, Perry A, Reifenberger G, Von Deimling A, Figarella-Branger D, Cavenee WK, Ohgaki H, Wiestler OD, Kleihues P, Ellison DW (2016) The 2016 world health organization classification of tumors of the central nervous system: a summary. Acta Neuropathologica 131:803–820
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444
Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828
Bengio Y et al. (2009) Learning deep architectures for AI, Foundations and trends® in Machine Learning 2(1):1–127
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533–536
Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639):115–118
Miotto R, Wang F, Wang S, Jiang X, Dudley JT (2018) Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform 19(6):1236–1246
Ahmed S, Nobel SN, Ullah O (2023) An effective deep CNN model for multiclass brain tumor detection using mri images and shap explainability. In: 2023 International conference on electrical, computer and communication engineering (ECCE), IEEE, 2023, pp 1–6
Jin W, Li X, Fatehi M, Hamarneh G (2023) Generating post-hoc explanation from deep neural networks for multi-modal medical image analysis tasks. MethodsX 10:102009
Kamnitsas K, Ledig C, Newcombe VF, Simpson JP, Kane AD, Menon DK, Rueckert D, Glocker B (2017) Efficient multi-scale 3d CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal 36:61–78
Bechelli S (2022) Computer-aided cancer diagnosis via machine learning and deep learning: a comparative review, arXiv preprint arXiv:2210.11943
Sharma S, Gupta S, Gupta D, Juneja A, Khatter H, Malik S, Bitsue ZK (2022) Deep learning model for automatic classification and prediction of brain tumor. J Sens
Kukreja V, Ahuja S et al. (2021) Recognition and classification of mathematical expressions using machine learning and deep learning methods. In: 2021 9th International conference on reliability, infocom technologies and optimization (Trends and Future Directions) (ICRITO), IEEE, 2021, pp 1–5
Thapa K, Khan H, Singh TG, Kaur A (2021) Traumatic brain injury: mechanistic insight on pathophysiology and potential therapeutic targets. J Mol Neurosci 71(9):1725–1742
Rehni AK, Singh TG, Jaggi AS, Singh N (2008) Pharmacological preconditioning of the brain: a possible interplay between opioid and calcitonin gene related peptide transduction systems. Pharmacol Reports 60(6):904
Kamini, Rani S (2023) Artificial intelligence and machine learning models for diagnosing neurodegenerative disorders. In: Data analysis for neurodegenerative disorders, Springer, pp 15–48
Ribeiro MT, Singh S, Guestrin C (2016) why should i trust you?” explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, 2016, pp 1135–1144
Lundberg SM, Lee S-I (2017) A unified approach to interpreting model predictions. In: Advances in neural information processing systems, pp 30
Sundararajan M, Taly A, Yan Q (2017) Axiomatic attribution for deep networks. In: International conference on machine learning, PMLR, 2017, pp 3319–3328
Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision, pp 618–626
Pertzborn D, Arolt C, Ernst G, Lechtenfeld OJ, Kaesler J, Pelzel D, Guntinas-Lichius O, von Eggeling F, Hoffmann F (2022) Multi-class cancer subtyping in salivary gland carcinomas with maldi imaging and deep learning. Cancers 14(17):4342
Gaur L, Bhandari M, Razdan T, Mallik S, Zhao Z (2022) Explanation-driven deep learning model for prediction of brain tumour status using MRI image data. Front Genet 448
Park KH, Batbaatar E, Piao Y, Theera-Umpon N, Ryu KH (2021) Deep learning feature extraction approach for hematopoietic cancer subtype classification. Int J Environ Res Public Health 18(4):2197
Marmolejo-Saucedo JA, Kose U (2022) Numerical grad-cam based explainable convolutional neural network for brain tumor diagnosis. Mobile Netw Appl 1–10
Montavon G, Samek W, Mu¨ller K-R (2018) Methods for interpreting and understanding deep neural networks. Digital Signal Process 73:1–15
Doshi-Velez F, Kim B (2017) Towards a rigorous science of interpretable machine learning, arXiv preprint arXiv:1702.08608
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Lamba, K., Rani, S. (2024). Explainable Artificial Intelligence for Deep Learning Models in Diagnosing Brain Tumor Disorder. In: Sharma, D.K., Peng, SL., Sharma, R., Jeon, G. (eds) Micro-Electronics and Telecommunication Engineering. ICMETE 2023. Lecture Notes in Networks and Systems, vol 894. Springer, Singapore. https://doi.org/10.1007/978-981-99-9562-2_13
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DOI: https://doi.org/10.1007/978-981-99-9562-2_13
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