Abstract
The use of Magnetic Resonance Images (MRI) is a frequently used tool in disease detection. The use of healthcare professionals to examine MRI images and to identify diseases are among traditional methods. Therefore, one way to improve clinical health care is to present and analyze medical images more efficiently and intelligently. Brain tumors can be of different types, and accordingly, they can cause serious health problems in adults and children. Such bulks can occur anywhere in the brain in different sizes and densities. This is not a standardized situation due to its nature. The diagnoses are revealed by the experts by analyzing the tumor images manually. In the proposed model, it is aimed at automating the process and reducing human errors in the system. The model is based on the deep learning technique, which is a probabilistic neural network to identify unwanted masses in the brain. In this study, a model has been created with VGG and CNN (Convolutional Neural Network) architectures, which are among the deep learning techniques. The performance values of the model outputs, accuracy, error rates, and specificity separators are discussed comparatively.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sangeetha R, Mohanarathinam A, Aravindh G, Jayachitra S, Bhuvaneswari M (2020) Automatic detection of brain tumor using deep learning algorithms. In: Proceedings of 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, pp 1–4. https://doi.org/10.1109/ICECA49313.2020.9297536
Santos J, Santos dos HDP, Vieira R (2020) Fall detection in clinical notes using language models and token classifier. In: Proceedings of 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS) Rochester, MN, USA, pp 283–288. https://doi.org/10.1109/CBMS49503.2020.00060
Sufri NAJ, Rahmad NA, Ghazali NF, Shahar N, As’ari MA (2019) Vision based system for banknote recognition using different machine learning and deep learning approach. In: Proceedings of 2019 IEEE 10th Control and System Graduate Research Colloquium (ICSGRC) Shah Alam, Malaysia, pp 5–8. https://doi.org/10.1109/ICSGRC.2019.8837068
El kaitouni SEI, Tairi H (2020) Segmentation of medical images for the extraction of brain tumors: a comparative study between the Hidden Markov and deep learning approaches. In: Proceedings of 2020 International Conference on Intelligent Systems and Computer Vision (ISCV), Fez, Morocco, pp 1–5. https://doi.org/10.1109/ISCV49265.2020.9204319
RamÃrez I, MartÃn A, Schiavi E (2018) Optimization of a variational model using deep learning: an application to brain tumor segmentation. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) Washington, DC, USA, pp 631–634. https://doi.org/10.1109/ISBI.2018.8363654
Sobhaninia Z, Rezaei S, Karimi N, Emami A, Samavi S (2020) Brain tumor segmentation by cascaded deep neural networks using multiple image scales. In Proceedings of 2020 28th Iranian Conference on Electrical Engineering (ICEE), Tabriz, Iran, pp 1–4. https://doi.org/10.1109/ICEE50131.2020.9260876
Wu P, Chang Q (2020) Brain tumor segmentation on multimodal 3D-MRI using deep learning method. In: Proceedings of 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Chengdu, China, pp 635–639. https://doi.org/10.1109/CISP-BMEI51763.2020.9263614
Chakrabarty N (2019) Brain mri images for brain tumor detection. Retrieved April 10, 2021, from https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection
Basaveswara SK (2019) CNN Architectures, a Deep-dive - towards data science. Medium. https://towardsdatascience.com/cnn-architectures-a-deep-dive-a99441d18049
Stursa D, Dolezel P (2019) Comparison of ReLU and linear saturated activation functions in neural network for universal approximation. In: Proceedings of 2019 22nd International Conference on Process Control (PC19), Strbske Pleso, Slovakia, pp 146–151. https://doi.org/10.1109/PC.2019.8815057
Kirana KC, Wibawanto S, Hidayah N, Cahyono GP, Asfani K (2019) Improved neural network using Integral-RELU based prevention activation for face detection. In: Proceedings of 2019 International Conference on Electrical, Electronics and Information Engineering (ICEEIE), Denpasar, Indonesia, pp 260–263. https://doi.org/10.1109/ICEEIE47180.2019.8981443
Galindo O, Ayub C, Ceberio M, Kreinovich V (2019) Faster quantum alternative to softmax selection in deep learning and deep reinforcement learning. 2019 IEEE Symposium Series on Computational Intelligence (SSCI) Xiamen, China, pp 815–818. https://doi.org/10.1109/SSCI44817.2019.9003167
Alabassy B, Safar M, El-Kharashi MW (2020) A high-accuracy implementation for softmax layer in deep neural networks. 2020 15th Design & Technology of Integrated Systems in Nanoscale Era (DTIS) Marrakech, Morocco, pp 1–6. https://doi.org/10.1109/DTIS48698.2020.9081313
Steiniger Y, Stoppe J, Meisen T, Kraus D (2020) Dealing with highly unbalanced sidescan sonar image datasets for deep learning classification tasks. Global Oceans, 2020 Singapore–U.S Gulf Coast, Biloxi, MS, USA, pp 1–7. https://doi.org/10.1109/IEEECONF38699.2020.9389373
Team K (n.d.) Simple. Flexible. Powerful. Retrieved from https://keras.io/
Allibhai E (2019) Building a Convolutional Neural Network (CNN) in Keras. Medium. https://towardsdatascience.com/building-a-convolutional-neural-network-cnn-in-keras-329fbbadc5f5
Chetty G, Singh M, White M (2019) Automatic brain image analysis based on multimodal deep learning scheme. In: Proceedings of International Conference on Machine Learning and Data Engineering (iCMLDE) 2019, pp 97–100. https://doi.org/10.1109/iCMLDE49015.2019.00028
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kırelli, Y., Arslankaya, S., Alcan, P. (2022). MRI Image Analysis with Deep Learning Methods in Brain Tumor Diagnosis. In: Sen, Z., Oztemel, E., Erden, C. (eds) Recent Advances in Intelligent Manufacturing and Service Systems. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-7164-7_4
Download citation
DOI: https://doi.org/10.1007/978-981-16-7164-7_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-7163-0
Online ISBN: 978-981-16-7164-7
eBook Packages: EngineeringEngineering (R0)