Abstract
Makine öğrenmesinin alt sınıfı olan derin öğrenme, birden çok katman ile ham veriden özelliklerin çıkarılmasını sağlamaktadır. Son yıllardaki teknolojik gelişmeler ile özellikle sağlık alanındaki görüntü işleme çalışmalarında sıklıkla tercih edilmektedir. Başarılı sonuçlar elde etmek için derin öğrenme modellerindeki parametrelerin optimize edilmesi gerekir. Bu işlemin belli bir düzeyde yazılım bilgisi gerektirmesi, alana yeterince hâkim olmayan kişilere zorluk oluşturabilmektedir. Araştırmacılar, kodlama gerektirmemesi nedeniyle hazır derin öğrenme modellerini ve görsel araçları tercih edebilmektedirler. Bu çalışmada önerilen uygulama aracılığıyla, manyetik rezonans görüntüleme taramaları için kompleks derin öğrenme işlemlerinin doğrudan grafik arayüzü üzerinden gerçekleştirilmesi hedeflenmektedir. Uygulama; veri seçimi, ön işleme, model oluşturma, eğitim ve test ana modüllerinden oluşmaktadır. Önde gelen bazı derin öğrenme modelleri uygulamaya entegre edilmiş olarak sunulmaktadır. İzlenen uyumluluk tasarımı sayesinde gelecekte yeni mimarilerin de kolaylıkla eklenebilmesinin önü açılmıştır. Modüller, açık kaynak manyetik rezonans görüntüleme verisi aracılığıyla doğrulanarak uygulamanın test tabanlı geliştirilmesi sağlanmıştır. Fonksiyonellik doğrulama testlerinde üç boyutlu evrişimsel sinir ağı kullanılarak literatüre paralel şekilde %81 doğruluk oranı gözlemlenmiştir. Uygulamanın radyoloji uzmanları ve araştırmacılar gibi kullanıcılar tarafından karar destek amacıyla kullanılabileceği düşünülmektedir.
Keywords
References
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Abstract
Deep learning, a subclass of machine learning, enables the extraction of features from raw data through multiple layers. With the technological developments in recent years, it is widely preferred in medical image processing studies. Parameters in deep learning models are needed to be optimized to obtain accurate results. This process requires a certain level of software knowledge and can cause difficulties for people who do not have sufficient proficiency. Researchers may prefer readily available deep learning models and visual tools as these do not require coding. It is proposed in this study that users can perform complex deep learning processes for magnetic resonance imaging data directly through the graphical interface of the application. The software tool consists of data selection, pre-processing, model creation, training, and test main modules. Some popular deep learning models are integrated into the application. New model architectures can be easily added for future releases, thanks to the compatibility design. The modules are validated via open-source magnetic resonance imaging data, and in this way, test-driven development is achieved. In the functionality validation tests performed, accuracy rate of 81% is observed similar to the literature by using three-dimensional convolutional neural network. It is thought that radiology experts and researchers can take advantage of the application for decision support purposes.
Keywords
References
- Akundi, A. (2018). A Deep Learning Graphical User Interface Application on MATLAB.
- Alzheimer's Association. (2021). What is Alzheimer’s Disease?. Çevrimiçi: https://www.alz.org/alzheimers-dementia/what-is-alzheimers (Erişim tarihi: 20.01.2021).
- Arnold, T. B. (2017). kerasR: R interface to the keras deep learning library. Journal of Open Source Software, 2(14), 296.
- Bucholc, M., Ding, X., Wang, H., Glass, D. H., Wang, H., Prasad, G., . . . Todd, S. (2019). A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual. Expert systems with applications, 130, 157-171.
- Chollet, F. (2018). Keras: The python deep learning library. Astrophysics Source Code Library, ascl: 1806.1022.
- Feng, C., Elazab, A., Yang, P., Wang, T., Zhou, F., Hu, H., . . . Lei, B. (2019). Deep learning framework for Alzheimer’s disease diagnosis via 3D-CNN and FSBi-LSTM. IEEE Access, 7, 63605-63618.
- Fischl, B. (2012). FreeSurfer. Neuroimage, 62(2), 774-781.
- Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: an update. ACM SIGKDD explorations newsletter, 11(1), 10-18.
- Itzcovich, I. (2018). DeepBrain. Çevrimiçi: https://github.com/iitzco/deepbrain (Erişim tarihi: 15.03.2020).
- Jo, T., Nho, K., & Saykin, A. J. (2019). Deep learning in Alzheimer's disease: diagnostic classification and prognostic prediction using neuroimaging data. Frontiers in aging neuroscience, 11, 220.
- Klemm, S., Scherzinger, A., Drees, D., & Jiang, X. (2018). Barista-a graphical tool for designing and training deep neural networks. arXiv preprint arXiv:1802.04626.
- Lang, S., Bravo-Marquez, F., Beckham, C., Hall, M., & Frank, E. (2019). Wekadeeplearning4j: A deep learning package for weka based on deeplearning4j. Knowledge-Based Systems, 178, 48-50.
- Liu, M., Cheng, D., Yan, W., & Alzheimer’s Disease Neuroimaging Initiative. (2018). Classification of Alzheimer’s disease by combination of convolutional and recurrent neural networks using FDG-PET images. Frontiers in neuroinformatics, 12, 35.
- Milde, S., Liebgott, A., Wu, Z., Feng, W., Yang, J., Mauch, L., . . . Gatidis, S. (2018). Graphical User Interface for Medical Deep Learning-Application to Magnetic Resonance Imaging. Paper presented at the 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC).
- Nalçakan, Y. (2018). Derin Öğrenme ile Alzheimer Hastalığının Teşhisi. (Yüksek Lisans). İstanbul Üniversitesi-Cerrahpaşa Lisansüstü Eğitim Enstitüsü, İstanbul.
- Noor, M. B. T., Zenia, N. Z., Kaiser, M. S., Al Mamun, S., & Mahmud, M. (2020). Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia. Brain informatics, 7(1), 1-21.
- Payan, A., & Montana, G. (2015). Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networks. arXiv preprint arXiv:1502.02506.
- Reinhold, J. C., Dewey, B. E., Carass, A., & Prince, J. L. (2019). Evaluating the impact of intensity normalization on MR image synthesis. Paper presented at the Medical Imaging 2019: Image Processing.
- Rubasinghe, I., & Meedeniya, D. (2020). Automated neuroscience decision support framework. In Deep Learning Techniques for Biomedical and Health Informatics (pp. 305-326): Elsevier.
- Von Chamier, L., Jukkala, J., Spahn, C., Lerche, M., Hernández-Pérez, S., Mattila, P., . . . Krull, A. (2020). ZeroCostDL4Mic: an open platform to simplify access and use of Deep-Learning in Microscopy. BioRxiv.
- Yamanakkanavar, N., Choi, J. Y., & Lee, B. (2020). MRI segmentation and classification of human brain using deep learning for diagnosis of alzheimer’s disease: a survey. Sensors, 20(11), 3243.
- Yeager, L., Bernauer, J., Gray, A., & Houston, M. (2015). Digits: the deep learning gpu training system. Paper presented at the ICML 2015 AutoML Workshop.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Computer Software |
| Journal Section | Research Articles |
| Authors | |
| Publication Date | June 20, 2021 |
| Submission Date | March 6, 2021 |
| Acceptance Date | May 10, 2021 |
| Published in Issue | Year 2021 Volume: 9 Issue: 2 |
