SISTEM DETEKSI PENYAKIT PADA OTAK DENGAN PENDEKATAN KLASIFIKASI CNN DAN PREPROCESSING IMAGE GENERATOR
DOI:
https://doi.org/10.23960/jitet.v12i3.4371Abstract Views: 728 File Views: 469
Abstract
In today's digital era, artificial intelligence technology has become an important part of various human activities, including in the healthcare sector. One of its focal points is the detection of brain diseases, which have significant implications for health and medical expenses. This study addresses the issue of accuracy in brain disease detection through the utilization of Convolutional Neural Network (CNN) methodology and preprocessing Image Generator. Previous research suggests that CNN with preprocessing Image Generator has the potential to enhance detection accuracy. The research employs the Computed Tomography (CT) of the Brain dataset from Kaggle, comprising 259 data points categorized into three classes: aneurysm, tumor, and cancer. Experimental findings indicate that the CNN method with preprocessing Image Generator yields higher accuracy in both training and testing phases, with reduced complexity. In conclusion, this method holds promise for more effective detection of brain diseasesDownloads
References
R. R. Karim, “IMPLEMENTASI KLASIFIKASI SENJATA TRADISIONAL JAWA BARAT MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN) DENGAN METODE TRANSFER LEARNING,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 2, Apr. 2024, doi: 10.23960/jitet.v12i2.4166.
H. A. Munira and M. S. Islam, “Hybrid Deep Learning Models for Multi-classification of Tumour from Brain MRI,” Journal of Information Systems Engineering and Business Intelligence, vol. 8, no. 2, pp. 162–174, Oct. 2022, doi: 10.20473/jisebi.8.2.162-174.
E. Roda and M. G. Bottone, “Editorial: Brain Cancers: New Perspectives and Therapies,” Front Neurosci, vol. 16, Feb. 2022, doi: 10.3389/fnins.2022.857408.
R. Islam, S. Imran, Md. Ashikuzzaman, and Md. M. A. Khan, “Detection and Classification of Brain Tumor Based on Multilevel Segmentation with Convolutional Neural Network,” J Biomed Sci Eng, vol. 13, no. 04, pp. 45–53, Apr. 2020, doi: 10.4236/jbise.2020.134004.
K. N. Qodri, I. Soesanti, and H. A. Nugroho, “Image Analysis for MRI-Based Brain Tumor Classification Using Deep Learning,” IJITEE (International Journal of Information Technology and Electrical Engineering), vol. 5, no. 1, p. 21, Jun. 2021, doi: 10.22146/ijitee.62663.
S. Chitnis, R. Hosseini, and P. Xie, “Brain tumor classification based on neural architecture search,” Sci Rep, vol. 12, no. 1, p. 19206, 2022, doi: 10.1038/s41598-022-22172-6.
M. Purnomo et al., “Brain Tumor Classification in MRI Images Using En-CNN,” International Journal of Intelligent Engineering and Systems, vol. 14, no. 4, pp. 437–451, Aug. 2021, doi: 10.22266/ijies2021.0831.38.
J. Cheng et al., “Correction: Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition,” PLoS One, vol. 10, no. 12, p. e0144479, Dec. 2015, doi: 10.1371/journal.pone.0144479.
IOMC World, “Aneurysm,” Journal of Health and Medical Research. Accessed: Apr. 26, 2024. [Online]. Available: https://www.iomcworld.org/medical-journals/aneurysm-54193.html
R. Andre, B. Wahyu, and R. Purbaningtyas, “KLASIFIKASI TUMOR OTAK MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK DENGAN ARSITEKTUR EFFICIENTNET-B3,” 2021. [Online]. Available: https://jurnal.umj.ac.id/index.php/just-it/index
B. Sri, E. Dwi, D. Rosal, and I. M. Setiadi, “Deteksi Tumor Otak dengan Metode Convolutional Neural Network”, doi: 10.30864/eksplora.v13i2.971.
S. Solanki, U. P. Singh, S. S. Chouhan, and S. Jain, “Brain Tumor Detection and Classification Using Intelligence Techniques: An Overview,” IEEE Access, vol. 11, pp. 12870–12886, Jan. 2023, doi: 10.1109/ACCESS.2023.3242666.
National Institutes of Health, “What Is Cancer?,” National Cancer Institute. Accessed: Apr. 26, 2024. [Online]. Available: https://www.cancer.gov/about-cancer/understanding/what-is-cancer
Z. Hu, J. Tang, Z. Wang, K. Zhang, L. Zhang, and Q. Sun, “Deep learning for image-based cancer detection and diagnosis − A survey,” Pattern Recognit, vol. 83, pp. 134–149, Nov. 2018, doi: 10.1016/j.patcog.2018.05.014.
S. Kato and K. Hotta, “Automatic enhancement preprocessing for segmentation of low quality cell images,” Sci Rep, vol. 14, no. 1, p. 3619, Feb. 2024, doi: 10.1038/s41598-024-53411-7.
E. Jacob, “Classification and Categorization: A Difference that Makes a Difference,” Libr Trends, vol. 52, Dec. 2004, Accessed: Apr. 26, 2024. [Online]. Available: https://www.researchgate.net/publication/32956263_Classification_and_Categorization_A_Difference_that_Makes_a_Difference
R. Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, “Convolutional neural networks: an overview and application in radiology,” Insights Imaging, vol. 9, no. 4, pp. 611–629, Aug. 2018, doi: 10.1007/s13244-018-0639-9.
Data Training, “Computed Tomography (CT) of the Brain,” Kaggle. Accessed: Apr. 28, 2024. [Online]. Available: https://www.kaggle.com/datasets/trainingdatapro/computed-tomography-ct-of-the-brain?select=files
