Medical Image Classification of Brain Tumors using Convolutional Neural Network Algorithm

 Alwas Muis (Universitas Ahmad Dahlan, Yogyakarta, Indonesia)
 (*)Sunardi Sunardi Mail (Universitas Ahmad Dahlan, Yogyakarta, Indonesia)
 Anton Yudhana (Universitas Ahmad Dahlan, Yogyakarta, Indonesia)

(*) Corresponding Author

Submitted: October 29, 2023; Published: January 9, 2024

Abstract

Brain tumor is a highly dangerous and deadly disease. It can occur due to the abnormal growth of cells or tissues in the head. Treatment for brain tumor is done with surgery and chemotherapy aimed at killing or destroying the cells that affect the growth process of brain tumor. Diagnosis of brain tumor is done using medical scans such as MRI, CT Scan, and PET Scan by analyzing the resulting images. Another method used to detect brain tumors is through biopsy, which is a process of taking cells or tissue from the body for examination in the laboratory. However, this method takes a long time because the cells taken from the patient will be examined in the laboratory. Therefore, a technique is needed to speed up accurate brain tumor diagnosis in order to obtain quick treatment. Machine learning can solve this problem with the classification of images produced by MRI. The classification technique that can be used is the GoogLeNet architecture in CNN. Because GoogLeNet is the algorithm that won the ImageNet Large Scale Visual Recognition Challenge (ILSVC) in 2014 The purpose of this study is to classify brain images using the GoogLeNet architecture. The dataset used in this study consists of 7023 images, consisting of 6320 images for training the model and 703 for testing the model. The results of this study obtained an accuracy percentage of 96%. This result is higher than previous studies that obtained an accuracy value of 94%.

Keywords


Brain Tumor; Classification; CNN; GoogLeNet; Machine Learning; MRI

Full Text:

PDF


Article Metrics

Abstract view : 376 times
PDF - 176 times

References

S. Angeli, K. E. Emblem, P. Due-Tonnessen, dan T. Stylianopoulos, Towards patient-specific modeling of brain tumor growth and formation of secondary nodes guided by DTI-MRI, NeuroImage Clin., vol. 20, no. August, hal. 664673, 2018, doi: 10.1016/j.nicl.2018.08.032.

F. Shi dkk., Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19, IEEE Rev. Biomed. Eng., vol. 14, hal. 415, 2021, doi: 10.1109/RBME.2020.2987975.

S. Das, O. F. M. R. R. Aranya, dan N. N. Labiba, Brain Tumor Classification Using Convolutional Neural Network, 1st Int. Conf. Adv. Sci. Eng. Robot. Technol. 2019, ICASERT 2019, no. May 2019, hal. 16, 2019, doi: 10.1109/ICASERT.2019.8934603.

M. Takahashi dkk., Eribulin penetrates brain tumor tissue and prolongs survival of mice harboring intracerebral glioblastoma xenografts, Cancer Sci., vol. 110, no. 7, hal. 22472257, 2019, doi: 10.1111/cas.14067.

K. D. Miller dan Q. T. Ostrom, Brain and Other Central Nervous System Tumor Statistics , vol. 71, no. 5, hal. 381406, 2021, doi: 10.3322/caac.21693.

R. Mulyadi, A. A. Islam, B. Murtala, J. Tammase, M. Hatta, dan M. Firdaus, Diagnostic yield of the combined magnetic resonance imaging and magnetic resonance spectroscopy to predict malignant brain tumor, Bali Med. J., vol. 9, no. 1, hal. 239245, 2020, doi: 10.15562/bmj.v9i1.1486.

A. Rayan dkk., Utilizing CNN-LSTM techniques for the enhancement of medical systems, Alexandria Eng. J., vol. 72, hal. 323338, 2023, doi: 10.1016/j.aej.2023.04.009.

S. Sunardi, A. Yudhana, dan A. R. WindraPutri, Mass Classification of Breast Cancer Using CNN and Faster R-CNN Model Comparison, Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, vol. 4, no. 3, 2022, doi: 10.22219/kinetik.v7i3.1462.

M. A. Hasan, Y. Riyanto, dan D. Riana, Grape leaf image disease classification using CNN-VGG16 model, J. Teknol. dan Sist. Komput., vol. 9, no. 4, hal. 218223, 2021, doi: 10.14710/jtsiskom.2021.14013.

M. R. Ismael dan I. Abdel-Qader, Brain Tumor Classification via Statistical Features and Back-Propagation Neural Network, IEEE Int. Conf. Electro Inf. Technol., vol. 2018-May, hal. 252257, 2018, doi: 10.1109/EIT.2018.8500308.

A. Pashaei, H. Sajedi, dan N. Jazayeri, Brain tumor classification via convolutional neural network and extreme learning machines, 2018 8th Int. Conf. Comput. Knowl. Eng. ICCKE 2018, no. Iccke, hal. 314319, 2018, doi: 10.1109/ICCKE.2018.8566571.

C. Szegedy dkk., Going Deeper with Convolutions, 2014.

Y. Xie dkk., Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives, Diagnostics, vol. 12, no. 8, 2022, doi: 10.3390/diagnostics12081850.

X. Zhao, Y. Wu, G. Song, Z. Li, Y. Zhang, dan Y. Fan, A deep learning model integrating FCNNs and CRFs for brain tumor segmentation, Med. Image Anal., vol. 43, hal. 98111, 2018, doi: 10.1016/j.media.2017.10.002.

D. O. Enoma, J. Bishung, T. Abiodun, O. Ogunlana, dan V. C. Osamor, Machine learning approaches to genome-wide association studies, J. King Saud Univ. - Sci., vol. 34, no. 4, hal. 101847, 2022, doi: 10.1016/j.jksus.2022.101847.

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

M. M. Hasan, H. Ali, M. F. Hossain, dan S. Abujar, Preprocessing of Continuous Bengali Speech for Feature Extraction, 2020 11th Int. Conf. Comput. Commun. Netw. Technol. ICCCNT 2020, hal. 14, 2020, doi: 10.1109/ICCCNT49239.2020.9225469.

A. Peryanto, A. Yudhana, dan R. Umar, Convolutional Neural Network and Support Vector Machine in Classification of Flower Images, Khazanah Inform. J. Ilmu Komput. dan Inform., vol. 8, no. 1, hal. 17, 2022, doi: 10.23917/khif.v8i1.15531.

S. Alzughaibi dan S. El Khediri, A Cloud Intrusion Detection Systems Based on DNN Using Backpropagation and PSO on the CSE-CIC-IDS2018 Dataset, Appl. Sci., vol. 13, no. 4, hal. 2276, 2023, doi: https://doi.org/10.3390/app13042276.

A. T. Handoyo dan G. P. Kusuma, Severity Classification of Diabetic Retinopathy Using Ensemble Stacking Method, Rev. dIntelligence Artif., vol. 36, no. 6, hal. 881887, 2022, doi: 10.18280/ria.360608.

S. K. Baranwal, K. Jaiswal, K. Vaibhav, A. Kumar, dan R. Srikantaswamy, Performance analysis of Brain Tumour Image Classification using CNN and SVM, Proc. 2nd Int. Conf. Inven. Res. Comput. Appl. ICIRCA 2020, hal. 537542, 2020, doi: 10.1109/ICIRCA48905.2020.9183023.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Medical Image Classification of Brain Tumors using Convolutional Neural Network Algorithm

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 JURNAL MEDIA INFORMATIKA BUDIDARMA

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.



JURNAL MEDIA INFORMATIKA BUDIDARMA
STMIK Budi Darma
Secretariat: Sisingamangaraja No. 338 Telp 061-7875998
Email: mib.stmikbd@gmail.com

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.