Brain Tumor Detection via Explainable Convolutional Neural Networks

Year 2021, Volume: 8 Issue: 3, 1323 - 1337, 30.09.2021

Abdullah Orman Utku Köse Tuncay Yiğit

https://doi.org/10.31202/ecjse.924446

Cited By: 3

Abstract

Especially since the early 2000s, deep learning techniques have been known as the most important actors of the field of artificial intelligence. Although these techniques are widely used in many different areas, their successful performance in the field of healthcare attracts more attention. However, the situation that these techniques are optimized with much more parameters than traditional machine learning techniques causes complex solution processes and they become opaque against human-sided perception level. For this reason, alternative studies have been carried out in order to make such black-box intelligent systems consisting of deep learning techniques reliable and understandable in terms of their limitations or error-making tendencies. As a result of the developments, the solutions that led to the introduce of a sub-field called as explainable artificial intelligence allow understanding whether the solutions offered by deep learning techniques are safe. In this study, a Convolutional Neural Networks (CNN) model was used for brain tumor detection and the safety level of that model could be understood through an explanatory module supported by the Class Activation Mapping (CAM). For the application process on the target data set, the developed CNN-CAM system achieved an average accuracy of 96.53%, sensitivity of 96.10% and specificity of 95.72%. Also, feedback provided by the doctors regarding the CAM visuals and the overall system performance showed that the CNN-CAM based solution was accepted positively. These findings reveal that the CNN-CAM system is reliable and understandable in terms of tumor detection.

Keywords

convolutional neural networks, explainable artificial intelligence, brain tumor, deep learning

Açıklanabilir Evrişimsel Sinir Ağları ile Beyin Tümörü Tespiti

Abstract

Derin öğrenme teknikleri özellikle 2000’li yılların başından bu yana yapay zeka alanının en önemli temsilcileri olarak bilinmektedir. Bu teknikler birçok farklı alanda yaygın bir biçimde kullanılıyor olsa da özellikle sağlık alanındaki başarılı performansları dikkatleri daha çok çekmektedir. Ancak bu tekniklerin geleneksel makine öğrenmesi tekniklerine göre çok daha fazla sayıda parametrelerle optimize ediliyor olması, çözüm süreçlerinin karmaşık olmasına ve insan taraflı algı düzeyine kapalı olmalarına sebep olmaktadır. Bu sebeple kara-kutu olarak da adlandırılan derin öğrenme tekniklerden oluşan zeki sistemleri insan gözünde güvenilir yapmak ve söz konusu sistemlerin sınırlılıklarını ya da hata yapma eğilimlerini anlayabilmek adına alternatif çalışmalar gerçekleştirilmeye başlanmıştır. Gelişmeler neticesinde açıklanabilir yapay zeka olarak adlandırılan bir alt-alanın doğmasına yol açan çözümler, derin öğrenme tekniklerinin sunduğu çözümlerin güvenli olup olmadığının anlaşılmasına olanak sağlamaktadır. Bu çalışmada, beyin tümörü tespiti için bir Evrişimsel Sinir Ağları (ESA) modeli kullanılmış ve modelin güvenlik düzeyi, Sınıf Aktivasyon Haritalama (SAH / CAM: Class Activation Mapping) destekli açıklanabilir bir modül üzerinden anlaşılabilmiştir. Geliştirilen ESA-SAH sistemi, hedef veri seti üzerindeki uygulama sürecinde ortalama %96,53 doğruluk, %96,10 duyarlılık ve %95,72 özgüllük sağlamıştır. Yine doktorların sistemdeki SAH görsellerine ve genel sistem performansına yönelik sundukları dönütler de ESA-SAH tabanlı çözümün pozitif yönde kabul edildiğini göstermiştir. Bu bulgular, ESA-SAH sisteminin tümör tespitinde güvenilir ve anlaşılır olduğunu ortaya koymaktadır.

Keywords

Evrişimsel sinir ağları, açıklanabilir yapay zeka, beyin tümörü, derin öğrenme

References

• Kumar, U., Yadav, S., “Application of Machine Learning to Analyse Biomedical Signals for Medical Diagnosis”, Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning, Vol. 1, IGI Global, Hershey: USA, (2021).
• Srivastava, R., Nguyen, N. G., Khanna, A., Bhattacharyya, S., “Predictive Intelligence in Biomedical and Health Informatics”, Vol. 2, De Gruyter, Blaufelden: Germany, (2020).
• Gupta, B. B., Sheng, Q. Z., “Machine Learning for Computer and Cyber Security: Principle, Algorithms, and Practices”, Vol. 1, CRC Press, Boca Raton: USA, (2019).
• Zhang, D., Tsai, J. J., “Advances in Machine Learning Applications in Software Engineering”, IGI Global, Hershey: USA, (2006).
• Güraksın, G. E., Ergün, U., Deperlioğlu, Ö., Classification of the heart sounds via artificial neural network, International Journal of Reasoning-based Intelligent Systems (IJRIS), 2020, 2 (3-4): 272-278.
• Boz, H., Köse, U., Emotion extraction from facial expressions by using artificial ıntelligence techniques, Broad Research in Artificial Intelligence and Neuroscience (BRAIN), 2018, 9 (1): 5-16.
• Köse, U., Arslan, A., Forecasting chaotic time series via anfis supported by vortex optimization algorithm: Applications on electroencephalogram time series, Arabian Journal for Science and Engineering (AJSE), 2017, 42 (8): 3103-3114.
• Aksu, N., Uçan, K., Zaman ve konum girdileri kullanılarak yapay sinir ağlarıyla referans evapotranspirasyonun tahmin edilmesi, El-Cezeri Journal of Science and Engineering, 2016, 3 (2): 204-221.
• Şentürk, A., Şentürk, Z. K., Yapay sinir ağları ile göğüs kanseri tahmini, El-Cezeri Journal of Science and Engineering, 2016, 3 (2): 345-350.
• Sivari, E., Civelek, Z., Genel anestezide kullanılan propofolün başlangıç dozunun bulanık mantık ile tahmini, El-Cezeri Journal of Science and Engineering, 2019, 6 (3): 808-816.
• Hutter, F., Kotthoff, L., Vanschoren, J., “Automated Machine Learning: Methods, Systems, Challenges”, Springer Nature, Heidelberg: Germany, (2019).
• Burkov, A., “Machine Learning Engineering”, Vol. 1, True Positive Incorporated, Quebec: Canada, (2020).
• Choi, R. Y., Coyner, A. S., Kalpathy-Cramer, J., Chiang, M. F., Campbell, J. P. Introduction to machine learning, neural networks, and deep learning, Translational Vision Science & Technology (TVST), 2020, 9 (2): 14-14.
• Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y., “Deep Learning”, Vol. 1, MIT Press, Cambridge: USA, (2016).
• LeCun, Y., “Deep learning hardware: Past, present, and future”, IEEE International Solid-State Circuits Conference-(ISSCC), San Francisco: USA, 12-19, (2019).
• Çakıroğlu, M. A., Süzen, A. A., Assessment and application of deep learning algorithms in civil engineering, El-Cezeri Journal of Science and Engineering, 2020, 7 (2): 906-922.
• Dara, S., Tumma, P., “Feature extraction by using deep learning: A survey”, International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore: India, 1795-1801, (2018).
• Du, X., Cai, Y., Wang, S., Zhang, L., “Overview of deep learning”, Youth Academic Annual Conference of Chinese Association of Automation (YAC), Wuhan: China, 159-164, (2016).
• Jiao, L., Zhao, J., A survey on the new generation of deep learning in image processing, IEEE Access, 2019, 7: 172231-172263.
• Özsoy, K., Aksoy, B., Salman, O. K. M., Investigation of the dimensional accuracy using image processing techniques in powder bed fusion, Institution of Mechanical Engineers Journal of Process Mechanical Engineering-Part E (JPME), 2021, E: 09544089211011011.
• Hassaballah, M., Awad, A. I., “Deep Learning in Computer Vision: Principles and Applications”, Vol. 1, CRC Press, Boca Raton: USA, (2020).
• Albawi, S., Mohammed, T. A., Al-Zawi, S., “Understanding of a convolutional neural network”, International Conference on Engineering and Technology (ICET), Antalya: Turkey, 1-6, (2017).
• Lee, H., Song, J., Introduction to convolutional neural network using Keras; an understanding from a statistician, Communications for Statistical Applications and Methods (CSAM), 2019, 26 (6): 591-610.
• Lu, L., Zheng, Y., Carneiro, G., Yang, L., Deep learning and convolutional neural networks for medical image computing, Advances in Computer Vision and Pattern Recognition (ACVPR), 2017, 10: 978-983.
• Sewak, M., Karim, M. R., Pujari, P., “Practical Convolutional Neural Networks: Implement Advanced Deep Learning Models Using Python”, Vol. 1, Packt Publishing Ltd, Birmingham: UK, (2018).
• Ren, Y., Cheng, X., “Review of convolutional neural network optimization and training in image processing”, International Symposium on Precision Engineering Measurements and Instrumentation, Chongqing: China, 1105331, (2019).
• Etyemez, A., Kurt, M., Yapay sinir ağları yöntemi ile optimum takım seçimi, El-Cezeri Journal of Science and Engineering, 2019, 6 (2): 323-332.
• Kaya, H., Paralel bağlı vorteks tüplerinin performansı için yapay sinir ağları analizi, El-Cezeri Journal of Science and Engineering, 2020, 7 (3): 1509-1517.
• Tschandl, P., Rosendahl, C., Akay, B. N., Argenziano, G., Blum, A., Braun, R. P., ..., Kittler, H., Expert-level diagnosis of nonpigmented skin cancer by combined convolutional neural networks, JAMA Dermatology, 2019, 155 (1): 58-65.
• Zhang, C., Sun, X., Dang, K., Li, K., Guo, X. W., Chang, J., ..., Zhong, W. Z., Toward an expert level of lung cancer detection and classification using a deep convolutional neural network, The Oncologist, 2019, 24 (9): 1159.
• Yala, A., Lehman, C., Schuster, T., Portnoi, T., Barzilay, R., A deep learning mammography-based model for improved breast cancer risk prediction, Radiology, 2019, 292 (1): 60-66.
• Biswal, S., Sun, H., Goparaju, B., Westover, M. B., Sun, J., Bianchi, M. T., Expert-level sleep scoring with deep neural networks, Journal of the American Medical Informatics Association, 2018, 25 (12): 1643-1650.
• Shao, K., Zhang, Z., He, S., Bo, X., “DTIGCCN: Prediction of drug-target interactions based on GCN and CNN”, IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Portland: USA, 337-342, (2020).
• Lee, S., Woo, S., Yu, J., Seo, J., Lee, J., Lee, C., Automated CNN-based tooth segmentation in cone-beam ct for dental implant planning, IEEE Access, 2020, 8: 50507-50518.
• Yarğı, V., Postalcıoğlu, S., EEG işareti kullanılarak bağımlılığa yatkınlığın makine öğrenmesi teknikleri ile analizi, El-Cezeri Journal of Science and Engineering, 2021, 8 (1): 142-154.
• Deepal, D. A. A., Fernando, T. G. I., “Convolutional Neural Network Approach for the Detection of Lung Cancers in Chest X-Ray Images”, Deep Learning for Cancer Diagnosis, Vol. 1, Springer Nature, Heidelberg: Germany, (2021).
• Ismail, W. N., Hassan, M. M., Alsalamah, H. A., Fortino, G., CNN-based health model for regular health factors analysis in internet-of-medical things environment, IEEE Access, 2020, 8: 52541-52549.
• Zhang, Y., Lobo-Mueller, E. M., Karanicolas, P., Gallinger, S., Haider, M. A., Khalvati, F., CNN-based survival model for pancreatic ductal adenocarcinoma in medical imaging, BMC Medical Imaging, 2020, 20 (1): 1-8.
• Gerlach, S., Fürweger, C., Hofmann, T., Schlaefer, A., Feasibility and analysis of CNN‐based candidate beam generation for robotic radiosurgery, Medical Physics, 2020, 47 (9): 3806-3815.
• Castelvecchi, D., Can we open the black box of AI?. Nature News, 2016, 538 (7623): 20.
• Rai, A., Explainable AI: From black box to glass box, Journal of the Academy of Marketing Science (JAMS), 2020, 48 (1): 137-141.
• Tjoa, E., Guan, C., A survey on explainable artificial intelligence (xai): Toward medical xai, IEEE Transactions on Neural Networks and Learning Systems, 2020.
• Adadi, A., Berrada, M., Peeking inside the black-box: A survey on explainable artificial intelligence (xai), IEEE Access, 2018, 6: 52138-52160.
• Xu, F., Uszkoreit, H., Du, Y., Fan, W., Zhao, D., Zhu, J., “Explainable AI: A brief survey on history, research areas, approaches and challenges”, CCF International Conference on Natural Language Processing and Chinese Computing, Zhengzhou: China, 563-574, (2019).
• Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., Yang, G. Z., XAI-Explainable artificial intelligence, Science Robotics, 2019, 4 (37).
• Core, M. G., Lane, H. C., Van Lent, M., Gomboc, D., Solomon, S., Rosenberg, M. “Building explainable artificial intelligence systems”, USA National Conference on Artificial Intelligence (AAAI), Boston: USA, 1766-1773, (2006).
• Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., ..., Herrera, F., Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible AI, Information Fusion, 2020, 58: 82-115.
• Köse, U., Are we safe enough in the future of artificial intelligence? A discussion on machine ethics and artificial intelligence safety, Broad Research in Artificial Intelligence and Neuroscience (BRAIN), 2018, 9 (2): 184-197.
• Sun, W., Tseng, T. L. B., Zhang, J., Qian, W., Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data, Computerized Medical Imaging and Graphics (CMIG), 2017, 57: 4-9.
• Zhang, N., Cai, Y. X., Wang, Y. Y., Tian, Y. T., Wang, X. L., Badami, B., Skin cancer diagnosis based on optimized convolutional neural network, Artificial Intelligence in Medicine, 2020, 102: 101756.
• Köse, U., Deperlioğlu, Ö., Alzubi, J., Patrut, B., “A Brief View on Medical Diagnosis Applications with Deep Learning”, Deep Learning for Medical Decision Support Systems, Vol. 1, Springer Nature, Heidelberg: Germany, (2021).
• Köse, U., Alzubi, J., “Deep Learning for Cancer Diagnosis”, Vol. 1, Springer Nature, Heidelberg: Germany, (2020).
• Moon, W. K., Huang, Y. S., Hsu, C. H., Chien, T. Y. C., Chang, J. M., Lee, S. H., ..., Chang, R. F., Computer-aided tumor detection in automated breast ultrasound using a 3-D convolutional neural network, Computer Methods and Programs in Biomedicine, 2020, 190: 105360.
• Toğaçar, M., Ergen, B., Cömert, Z., BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model, Medical Hypotheses, 2020, 134: 109531.
• Khan, H., Shah, P. M., Shah, M. A., ul Islam, S., Rodrigues, J. J., Cascading handcrafted features and convolutional neural network for IoT-enabled brain tumor segmentation, Computer Communications (ComCom), 2020, 153: 196-207.
• Rehman, A., Khan, M. A., Saba, T., Mehmood, Z., Tariq, U., Ayesha, N., Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture, Microscopy Research and Technique, 2021, 84 (1): 133-149.
• Zhang, S., Bamakan, S. M. H., Qu, Q., Li, S., Learning for personalized medicine: a comprehensive review from a deep learning perspective, IEEE Reviews in Biomedical Engineering, 2018, 12: 194-208.
• Deperlioğlu, Ö., Classification of phonocardiograms with convolutional neural networks, Broad Research in Artificial Intelligence and Neuroscience (BRAIN), 2018, 9 (2): 22-33.
• Sagayam, K. M., Andrushia, A. D., Ghosh, A., Deperlioğlu, Ö., Elngar, A. A., Recognition of hand gesture image using deep convolutional neural network, International Journal of Image and Graphics (IJIG), 2021, 2140008.
• Köse, U., Deperlioğlu, Ö., Alzubi, J., Patrut, B., “Diagnosing Diabetic Retinopathy by Using a Blood Vessel Extraction Technique and a Convolutional Neural Network”, Deep Learning for Medical Decision Support Systems, Vol. 1, Springer Nature, Heidelberg: Germany, (2021).
• Samek, W., Montavon, G., Vedaldi, A., Hansen, L. K., Müller, K. R., Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, Vol. 1, Springer Nature, Heidelberg: Germany, (2019).
• Fu, K., Dai, W., Zhang, Y., Wang, Z., Yan, M., Sun, X., Multicam: Multiple class activation mapping for aircraft recognition in remote sensing images, Remote Sensing, 2019, 11 (5): 544.
• Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., ..., Van Leemput, K., The multimodal brain tumor image segmentation benchmark (BRATS), IEEE Transactions on Medical Imaging, 2014, 34 (10): 1993-2024.
• Lloyd, C. T., Sorichetta, A., & Tatem, A. J., High resolution global gridded data for use in population studies, Scientific Data, 2017, 4 (1): 1-17.
• Deperlioğlu, Ö., & Köse, U., “Diagnogsis of Diabete mellitus Using Deep Neural Network”, Medical Technologies National Congress (TIPTEKNO), Magusa: Cyprus, 1-4, (2018).
• Keskenler, M. F., Dal, D., Aydin, T., Yapay zeka destekli ÇOKS yöntemi ile kredi kartı sahtekarlığının tespiti, El-Cezeri Journal of Science and Engineering, 2021, 8 (2): 1007-1023.
• Öziç, M. Ü., Özşen, S., 3B alzheimer MR görüntülerinin hacimsel kayıp bölgelerindeki voksel değerleri kullanılarak sınıflandırılması, El-Cezeri Journal of Science and Engineering, 2020, 7 (3): 1152-1166.
• Kaya, D., Türk, M., Kaya, T., Examining the effect of dimension reduction on EEG signals by k-nearest neighbors algorithm, El-Cezeri Journal of Science and Engineering, 2018, 5 (2): 591-595.
• Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D., “Grad-cam: Visual explanations from deep networks via gradient-based localization”, IEEE International Conference on Computer Vision, Venice: Italy, 618-626, (2017).
• Fu, R., Hu, Q., Dong, X., Guo, Y., Gao, Y., Li, B., Axiom-based grad-cam: Towards accurate visualization and explanation of CNNs. arXiv preprint, 2020, arXiv: 2008.02312.
• Fukui, H., Hirakawa, T., Yamashita, T., Fujiyoshi, H., “Attention branch network: Learning of attention mechanism for visual explanation”, IEEE Conference on Computer Vision and Pattern Recognition, Long Beach: USA, 10705-10714, (2019).
• Rodrigues, J. J., Segundo, D. B. D. R., Junqueira, H. A., Sabino, M. H., Prince, R. M., Al-Muhtadi, J., De Albuquerque, V. H. C., Enabling technologies for the internet of health things, IEEE Access, 2018, 6: 13129-13141.
• Shankar, K., Perumal, E., Gupta, D., “Artificial Intelligence for the Internet of Health Things”, Vol. 1, CRC Press, Boca Raton: USA, (2021).
• Liu, N., Chee, M. L., Niu, C., Pek, P. P., Siddiqui, F. J., Ansah, J. P., ..., Ong, M. E. H., Coronavirus disease 2019 (COVID-19): An evidence map of medical literature, BMC Medical Research Methodology, 2020, 20 (1): 1-11.
• Süt, H. M., Öznaçar, B., Effects of COVID-19 period on educational systems and institutions, International Journal of Curriculum and Instruction (IJCI), 2021, 13 (1): 537-551.
• Blanchard, A. L., The effects of COVID-19 on virtual working within online groups, Group Processes & Intergroup Relations, 2021, 24 (2): 290-296.
• Liao, Q. V., Gruen, D., Miller, S., “Questioning the AI: informing design practices for explainable AI user experiences”, CHI Conference on Human Factors in Computing Systems, Honolulu: USA, 1-15, (2020).
• Gheisari, M., Alzubi, J., Zhang, X., Köse, U., Saucedo, J. A. M., A new algorithm for optimization of quality of service in peer to peer wireless mesh networks, Wireless Networks, 2020, 26 (7): 4965-4973.
• Aksoy, B., Salman, O. K. M., Detection of COVID-19 disease in chest x-ray images with capsul networks: Application with cloud computing, Journal of Experimental & Theoretical Artificial Intelligence, 2021, 1-15.
• Yalçın, N., Altun, Y., Köse, U., Educational material development model for teaching computer network and system management, Computer Applications in Engineering Education, 2015, 23 (4): 621-629.
• Hughes, R., Edmond, C., Wells, L., Glencross, M., Zhu, L., Bednarz, T., “eXplainable AI (xai) An introduction to the XAI landscape with practical examples”, ACM SIGGRAPH Asia Conference, Seoul: South Korea, 1-62, (2020).
• Alom, M. Z., Yakopcic, C., Hasan, M., Taha, T. M., Asari, V. K., Recurrent residual U-Net for medical image segmentation, Journal of Medical Imaging, 2019, 6 (1): 014006.
• Lu, S., Lu, Z., Zhang, Y. D., Pathological brain detection based on AlexNet and transfer learning, Journal of Computational Science (JCS), 2019, 30: 41-47.