Perbandingan Performa Arsitektur CNN Terhadap Klasifikasi Tumor Otak Menggunakan Data MRI
-
Sekar Dewi Harnum Saputri
*
Telkom University, Bandung,
Indonesia
- Achmad Lukman Telkom University, Bandung, Indonesia
- Muhamad Irsan Telkom University, Bandung, Indonesia (*) Corresponding Author
Abstract
This study discusses the performance comparison of four Convolutional Neural Network (CNN) architectures in brain tumor classification using histopathology images. CNN has proven its effectiveness in improving the accuracy and efficiency of image-based medical diagnosis. This study compares four popular architectures, namely ResNet, AlexNet, InceptionNet, and VGG12, using a histopathology image dataset with a total of 2,145 images divided into training (70%), validation (15%), and testing (15%) subsets. The results show that the VGG12 model achieves the best accuracy of 98.0%, followed by InceptionNet with an accuracy of 97.3%. The ResNet model achieves an accuracy of 94.3%, while AlexNet has an accuracy of 93.2%. In addition, the VGG12 model shows consistent performance with high precision, recall, and F1-Score values, making it a superior choice for medical applications. This study provides in-depth insights into the advantages and limitations of each CNN architecture, as well as implementation guidelines to support the development of image-based medical diagnosis applications efficiently and accurately.
Downloads
References
J. Ferlay et al., “Cancer statistics for the year 2020: An overview,” Int. J. Cancer, vol. 149, no. 4, pp. 778–789, 2021, doi: 10.1002/ijc.33588.
S. Srinivasan, D. Francis, S. K. Mathivanan, H. Rajadurai, B. D. Shivahare, and M. A. Shah, “A hybrid deep CNN model for brain tumor image multi-classification,” BMC Med. Imaging, vol. 24, no. 1, pp. 1–21, 2024, doi: 10.1186/s12880-024-01195-7.
M. A. Khan and R. B. Z. Auvee, “Comparative Analysis of Resource-Efficient CNN Architectures for Brain Tumor Classification,” 2024, [Online]. Available: http://arxiv.org/abs/2411.15596.
O. O. Oladimeji and A. O. J. Ibitoye, “Brain tumor classification using ResNet50-convolutional block attention module,” Appl. Comput. Informatics, 2023, doi: 10.1108/ACI-09-2023-0022.
C. Swarup, K. U. Singh, A. Kumar, S. K. Pandey, N. varshney, and T. Singh, “Brain tumor detection using CNN, AlexNet & GoogLeNet ensembling learning approaches,” Electron. Res. Arch., vol. 31, no. 5, pp. 2900–2924, 2023, doi: 10.3934/ERA.2023146.
A. A. Asiri et al., “Brain Tumor Detection and Classification Using Fine-Tuned CNN with ResNet50 and U-Net Model: A Study on TCGA-LGG and TCIA Dataset for MRI Applications,” Life, vol. 13, no. 7, 2023, doi: 10.3390/life13071449.
Z. N. K. Swati et al., “Brain tumor classification for MR images using transfer learning and fine-tuning,” Comput. Med. Imaging Graph., vol. 75, pp. 34–46, 2019, doi: 10.1016/j.compmedimag.2019.05.001.
H. A. Mengash and H. A. Hosni Mahmoud, “Brain cancer tumor classification from motion-corrected MRI images using convolutional neural network,” Comput. Mater. Contin., vol. 68, no. 2, pp. 1551–1563, 2021, doi: 10.32604/cmc.2021.016907.
S. Deepak and P. M. Ameer, “Brain tumour classification using siamese neural network and neighbourhood analysis in embedded feature space,” Int. J. Imaging Syst. Technol., vol. 31, no. 3, pp. 1655–1669, 2021, doi: 10.1002/ima.22543.
B. Lu et al., “A practical Alzheimer’s disease classifier via brain imaging-based deep learning on 85,721 samples,” J. Big Data, vol. 9, no. 1, 2022, doi: 10.1186/s40537-022-00650-y.
Z. Tian, J. Wang, M. Zhou, Y. Zhang, and M. Shi, “Retinal Medical Image Classification Based on Deep Convolutional Neural Network AlexNet,” 4th Int. Conf. Ind. Artif. Intell. IAI 2022, pp. 1–6, 2022, doi: 10.1109/IAI55780.2022.9976745.
R. Pillai, A. Sharma, N. Sharma, and R. Gupta, “Brain Tumor Classification using VGG 16, ResNet50, and Inception V3 Transfer Learning Models,” 2023 2nd Int. Conf. Innov. Technol. INOCON 2023, pp. 1–5, 2023, doi: 10.1109/INOCON57975.2023.10101252.
S. Chauhan et al., “BT-CNN: a balanced binary tree architecture for classification of brain tumour using MRI imaging,” Front. Physiol., vol. 15, no. April, pp. 1–15, 2024, doi: 10.3389/fphys.2024.1349111.
C. R. Prasad, S. Hussain, B. Srinivas, S. Samala, R. Janapati, and S. Yalabaka, “Brain Tumor Detection using modified VGG-19 and Inception ResnetV2 models,” Proc. 2023 IEEE 2nd Int. Conf. Ind. Electron. Dev. Appl. ICIDeA 2023, pp. 49–53, 2023, doi: 10.1109/ICIDeA59866.2023.10295200.
Y. Zhuge et al., “Automated glioma grading on conventional MRI images using deep convolutional neural networks,” Med. Phys., vol. 47, no. 7, pp. 3044–3053, 2020, doi: 10.1002/mp.14168.
M. M. Zahoor et al., “Brain Tumor MRI Classification Using a Novel Deep Residual and Regional CNN,” Biomedicines, vol. 12, no. 7, pp. 1–19, 2024, doi: 10.3390/biomedicines12071395.
Y. Xie et al., “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.
N. Kesav and M. G. Jibukumar, “Efficient and low complex architecture for detection and classification of Brain Tumor using RCNN with Two Channel CNN,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 8, pp. 6229–6242, 2022, doi: 10.1016/j.jksuci.2021.05.008.
E. C. Niswary, “Klasifikasi Citra Tumor Otak MRI Menggunakan Convolutional Neural Network Model InceptionResNet-V2,” Universitas Muhammadiyah Malang, 2024.
I. C. Sukandar, F. Tri Anggraeny, and M. Hanindia Prami Swari, “Effect of Optimisation in Brain Tumour Classification with CNN-VIT Hybrid,” Antivirus J. Ilm. Tek. Inform., vol. 18, no. 1, pp. 112–124, 2024, doi: 10.35457/antivirus.v18i1.3557.
K. N. Qodri, “Analisis Perbandingan Klasifikasi Tumor Otak Menggunakan Deep Learning,” IJITECH, vol. 2, no. 1, pp. 1–6, 2024, [Online]. Available: https://ojisnu.isnuponorogo.org/index.php/ijitech/article/view/112.
M. L. Septipalan, M. S. Hibrizi, N. Latifah, R. Lina, and F. Bimantoro, “Klasifikasi Tumor Otak Menggunakan CNN Dengan Arsitektur Resnet50,” Semin. Nas. Teknol. Sains, vol. 3, no. 1, pp. 103–108, 2024, doi: 10.29407/stains.v3i1.4357.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Perbandingan Performa Arsitektur CNN Terhadap Klasifikasi Tumor Otak Menggunakan Data MRI
Pages: 2735-2746
Copyright (c) 2025 Sekar Dewi Harnum Saputri, Achmad Lukman, Muhamad Irsan
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
