Identifikasi Tumor Otak Citra MRI dengan Convolutional Neural Network

Nur Nafiiyah

Abstract


The science of artificial intelligence and computer vision is beneficial in facilitating the detection of diseases in the medical field. Computer-based disease detection can save time. However, identifying and detecting tumors on MRI images require seriousness and is time-consuming. Due to the diversity of structures in size, shape, and intensity of the image, accuracy is needed in identifying the original organ structure and the diseased one. Previous studies have proposed a method for identifying brain tumors to produce the correct precision. In previous studies, neural network-based methods have good accuracy. We present five Convolutional Neural Network (CNN) architectures for identifying brain tumors (glioma, meningioma, no tumor, and pituitary) on MRI images. This study aims to develop an optimal CNN architecture for identifying tumors. We use the dataset from Kaggle with a total training data of 5712 and testing of 1311. Of the five proposed CNN architectures, architecture c has the highest accuracy of 82.2% with an unlimited number of parameters of 29605060. A good CNN architecture has many convolution layers. We also compare the proposed architecture with CNN transfer learning (Inception, ResNet-50, and VGG16), and with CNN transfer learning architecture, the accuracy is higher than our proposed architecture.

Keywords


arsitektur CNN, identifikasi tumor otak, MRI.

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References


N. Abiwinanda, M. Hanif, S. T. Hesaputra, A. Handayani, and T. R. Mengko, “Brain tumor classification using convolutional neural network,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11993 LNCS, pp. 335–342, 2020, doi: 10.1007/978-3-030-46643-5_33.

F. Akbar, A. N. Rais, I. A. Sobari, R. A. Zuama, and B. Rudiarto, “Analisis Performa Algoritma Naive Bayes pada Deteksi Otomatis Citra MRI,” JITK (Jurnal Ilmu Pengetah. dan Teknol. Komputer), vol. 5, no. 1, 2019, doi: 10.33480/jitk.v5i1.586.

A. Adinegoro, R. D. Atmaja, and R. Purnamasari, “Deteksi Tumor Otak dengan Ektrasi Ciri & Feature Selection mengunakan Linear Discriminant Analysis (LDA) dan Support Vector Machine (SVM),” e-Proceeding Eng., vol. 2, no. 2, pp. 2532–2539, 2015.

I. Soesanti, A. Susanto, T. Widodo, and M. Tjokronagoro, “Ekstraksi Ciri dan Identifikasi Citra Otak MRI Berbasis Eigenbrain Image,” Forum Tek., vol. 34, no. 1, 2011.

L. W. Astuti, “Ekstrasi Fitur Citra MRI Otak Menggunakan Data Wavelet Transform (DWT) untuk Klasifikasi Penyakit Tumor Otak,” J. Ilm. Inform. Glob., vol. 10, no. 2, pp. 80–86, 2019, doi: 10.36982/jig.v10i2.854.

S. Kumar, C. Dabas, and S. Godara, “Classification of Brain MRI Tumor Images: A Hybrid Approach,” Procedia Comput. Sci., vol. 122, pp. 510–517, 2017, doi: 10.1016/j.procs.2017.11.400.

N. Varuna Shree and T. N. R. Kumar, “Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network,” Brain Informatics, vol. 5, no. 1, 2018, doi: 10.1007/s40708-017-0075-5.

T. A. M and Q. N. Azizah, “Klasifikasi Tumor Otak Menggunakan Ekstraksi Fitur HOG dan Support Vector Machine,” vol. 4, no. 1, pp. 45–50, 2022.

W. Widhiarso, Y. Yohannes, and C. Prakarsah, “Brain Tumor Classification Using Gray Level Co-occurrence Matrix and Convolutional Neural Network,” IJEIS (Indonesian J. Electron. Instrum. Syst., vol. 8, no. 2, p. 179, 2018, doi: 10.22146/ijeis.34713.

M. Aamir et al., “A deep learning approach for brain tumor classification using MRI images,” Comput. Electr. Eng., vol. 101, no. May, p. 108105, 2022, doi: 10.1016/j.compeleceng.2022.108105.

Y. Gu and K. Li, “A Transfer Model Based on Supervised Multi-Layer Dictionary Learning for Brain Tumor MRI Image Recognition,” Front. Neurosci., vol. 15, 2021, doi: 10.3389/fnins.2021.687496.

M. C. Daniel and L. M. Ruxandra, “Brain Tumor Classification Using Pretrained Convolutional Neural Networks,” 2021 16th Int. Conf. Eng. Mod. Electr. Syst. EMES 2021 - Proc., vol. 11, no. September, pp. 1457–1461, 2021, doi: 10.1109/EMES52337.2021.9484102.

H. P. A. Tjahyaningtijas et al., “Brain Tumor Classification in MRI Images Using En-CNN,” Int. J. Intell. Eng. Syst., vol. 14, no. 4, 2021, doi: 10.22266/ijies2021.0831.38.

S. Deepak and P. M. Ameer, “Brain tumor classification using deep CNN features via transfer learning,” Comput. Biol. Med., vol. 111, no. June, p. 103345, 2019, doi: 10.1016/j.compbiomed.2019.103345.

P. Harish and S. Baskar, “MRI based detection and classification of brain tumor using enhanced faster R-CNN and Alex Net model,” Mater. Today Proc., no. xxxx, 2020, doi: 10.1016/j.matpr.2020.11.495.

E. Irmak, “Multi-Classification of Brain Tumor MRI Images Using Deep Convolutional Neural Network with Fully Optimized Framework,” Iran. J. Sci. Technol. - Trans. Electr. Eng., vol. 45, no. 3, 2021, doi: 10.1007/s40998-021-00426-9.

Kaggle, “Dataset MRI.” https://www.kaggle.com/masoudnickparvar/brain-tumor-mri-dataset.




DOI: https://doi.org/10.30591/jpit.v8i3.4985

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