KLASIFIKASI TUMOR OTAK PADA CITRA MRI MENGGUNAKAN ARSITEKTUR EFFICIENTNETB3 DAN MOBILENETV3
Article Sidebar
Read Counter : 8
Download : 4
Main Article Content
Abstract
Brain tumor is a disease that can occur not only in adults, but children can also suffer from brain tumors. This disease is malignant and can affect other tissues, so it is better if brain tumors can be detected early for effective treatment and prevent complications even death. To classify the types of brain tumors more efficiently, a Deep Learning algorithm, namely Convolutional Neural Network (CNN), is utilized. The CNN architecture used in this research are EfficientNetB3 and MobileNetV3. This research uses the Brain Tumor Classification dataset from Kaggle which contains 3264 images of brain tumors categorized into 4 classes, namely glioma tumor, meningioma tumor, no tumor, and pituitary tumor. The aim of this research is to compare the classification results of the CNN architecture used, to obtain the highest accuracy value. This study uses a quantitative approach to classify brain tumors. The training data accuracy results obtained from the EfficientNet-B3 and MobileNet-V3 architectures were 92.53% and 88.03% respectively, while the test data accuracy was 92.53% and 87% respectively. The highest accuracy result is MobileNet-V3, so this architecture is the effective for classifying brain tumors.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Menawarkan akses terbukaReferences
[1] K. Amelia, R. Magladena and S. Saidah, "Klasifikasi Penyakit Tumor Otak Pada Citra Mri Menggunakan Metode CNN Dengan Arsitektur Alexnet," eProceedings of Engineering, vol. 9, 2023.
[2] K. Deeksha, M. Deeksha, A. V. Girish, A. S. Bhat and H. Lakshmi, "Classification of Brain Tumor and its types using Convolutional Neural Network," in 2020 IEEE International Conference for Innovation in Technology (INOCON), 2020, pp. 1--6.
[3] A. S. Febrianti, T. A. Sardjono and A. F. Babgei, "Klasifikasi Tumor Otak pada Citra Magnetic Resonance Image dengan Menggunakan Metode Support Vector Machine," Jurnal Teknik ITS (SINTA: 4, IF: 1.1815), vol. 9, pp. A118--A123, 2020.
[4] S. R. Dewi, "Deep Learning Object Detection Pada Video Menggunakan Tensorflow Dan Convolutional Neural Network," 2018.
[5] R. A. Ramadhani, B. W. Pangestu and R. Purbaningtyas, "Klasifikasi Tumor Otak Menggunakan Convolutional Neural Network dengan Arsitektur EfficientNet-B3," JUST IT: Jurnal Sistem Informasi, Teknologi Informasi dan Komputer, vol. 12, pp. 55--59, 2022.
[6] I. Suta, R. S. Hartati and Y. Divayana, "Diagnosa tumor otak berdasarkan citra MRI (Magnetic Resonance Imaging)," Maj. Ilm. Teknol. Elektro, vol. 18, pp. 149--154, 2019.
[7] I. M. Faiza and W. Andriani, "Tinjauan Pustaka Sistematis: Penerapan Metode Machine Learning untuk Deteksi Bencana Banjir," Jurnal Minfo Polgan, vol. 11, pp. 59-63, 2022.
[8] J. Pujoseno, "Implementasi Deep Learning Menggunakan Convolutional Neural Network Untuk Klasifikasi Alat Tulis," 2018.
[9] M. N. Winnarto, M. Mailasari and A. Purnamawati, "Klasifikasi Jenis Tumor Otak Menggunakan Arsitekture Mobilenet V2," Jurnal SIMETRIS, vol. 13, pp. 1--12, 2022.
[10] W. R. PERDANI, R. MAGDALENA and N. K. C. PRATIWI, "Deep Learning untuk Klasifikasi Glaukoma dengan menggunakan Arsitektur EfficientNet," ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika, vol. 10, p. 322, 2022.
[11] J. Feriawan and D. Swanjaya, "Perbandingan Arsitektur Visual Geometry Group dan MobileNet Pada Pengenalan Jenis Kayu," Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi), vol. 4, pp. 185--190, 2020.
[12] J. Seetha and S. S. Raja, "Brain tumor classification using convolutional neural networks," Biomedical & Pharmacology Journal, vol. 11, p. 1457, 2018.
[13] F. A. A. Harahap, A. N. Nafisa, E. N. D. B. Purba and N. A. Putri, "Implementasi Algoritma Convolutional Neural Network Arsitektur Model MobileNetV2 dalam Klasifikasi Penyakit Tumor Otak Glioma, Pituitary dan Meningioma," Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA), vol. 5, pp. 53--61, 2023.
[14] W. Hastomo, "Convolution Neural Network Arsitektur Mobilenet-V2 Untuk Mendeteksi Tumor Otak," in Prosiding Seminar SeNTIK, vol. 5, 2021, pp. 17--21.
[15] M. Ramdhan, Metode penelitian, Cipta Media Nusantara, 2021.
[16] I. Fauzi and A. Moenir, "Klasifikasi Spesies Tanaman Magnolia Menggunakan Metode Convolutional Neural Networks," Journal of Artificial Intelligence and Innovative Applications, vol. 2, pp. 235-239, 2021.
[17] S. F. Handono, F. T. Anggraeny and B. Rahmat, "Implementasi Convolutional Neural Network (CNN) untuk Deteksi Retinopati Diabetik," Jurnal Informatika dan Sistem Informasi, vol. 1, pp. 669--678, 2020.
[18] A. I. Hanifah and A. Hermawan, "Klasifikasi Kematangan Pisang Menggunakan Metode Convolutional Neural Network," Komputika: Jurnal Sistem Komputer, vol. 12, pp. 49--56, 2023.
[19] M. I. Fikri, T. S. Sabrila and Y. Azhar, "Perbandingan metode na{"i}ve bayes dan support vector machine pada analisis sentimen twitter," SMATIKA Jurnal: STIKI Informatika Jurnal, vol. 10, pp. 71-76, 2020.
[20] K. Nugroho and S. Murdowo, "Klasifikasi Jenis Hewan Pada Kebun Binatang Dengan Menggunakan Metode Deep Neural Network," Jurnal Ilmiah Infokam, vol. 18, pp. 46-51, 2022.