Brain Tumor Classification using Convolutional Neural Network with ResNet Architecture
DOI:
https://doi.org/10.64878/jistics.v1i1.8Keywords:
brain tumor, CNN, image Classification, ResNetAbstract
Brain tumors are dangerous, sometimes fatal illnesses that require prompt, accurate diagnosis to enhance patient outcomes. Given the intricacy and diversity of tumor characteristics, manual interpretation of brain MRI data is frequently laborious and prone to human error. This research aims to create an automated system for classifying brain tumors by integrating the Convolutional Neural Network (CNN) algorithm with the ResNet architecture. The suggested approach makes use of 7,023 MRI pictures that have been divided into four categories: non-tumor, pituitary tumor, meningioma, and glioma. Image normalization, grayscale conversion, scaling, and data augmentation methods, including rotation and flipping, were among the preprocessing processes used to enhance model performance. The ResNet design was chosen because it effectively trains deeper networks by utilizing residual connections to prevent vanishing gradient problems. Metrics such as F1-score, accuracy, precision, and recall were used to train and assess the system. According to the testing data, the model performed consistently across all classes and attained an outstanding accuracy of 94.14%. These results validate the promise of deep learning methods, especially CNNs with ResNet enhancements, for classification tasks involving medical images. The system created in this work is a promising tool for assisting clinical decision-making, cutting down on diagnostic time, and improving the accuracy of brain tumor identification and classification.
Downloads
References
[1] A. S. Febrianti, “Klasifikasi Tumor Otak Pada Citra Magnetic Resonance Image (MRI) Dengan Menggunakan Metode Support Vektor Machine (SVM),” 2020.
[2] Y. Mehmood, “Brain tumor grade classification using the ConvNext architecture," 2024, doi: 10.1177/20552076241284920.
[3] S. Patil and D. Kirange, “ScienceDirect ScienceDirect Ensemble of Deep Learning Models for Brain Tumor Detection Ensemble of Deep Learning Models for Brain Tumor Detection International Conference on Machine Learning and Data Engineering,” Procedia Comput. Sci., vol. 218, no. 2022, pp. 2468–2479, 2023, doi: 10.1016/j.procs.2023.01.222.
[4] M. Islam, P. Barua, M. Rahman, T. Ahammed, and L. Akter, “Healthcare Analytics Transfer learning architectures with fine-tuning for brain tumor classification using magnetic resonance imaging,” vol. 4, no. October, 2023.
[5] S. Pariyasto, V. A. Warongan, S. Tinggi, I. Kesehatan, and M. Sejati, “Optimalisasi klasifikasi citra medis menggunakan cnn dan adam optimizer dengan paramater minimum,” vol. 4307, no. 1, pp. 691–701, 2025.
[6] R. G. Abdullah and M. Kurniawan, “CNN,” vol. 12, no. 3, 2024.
[7] A. J. Sinulingga, D. R. Manalu, and S. Manurung, “Klasifikasi Jenis Tumor Otak Berdasarkan Citra Glioma Menggunakan Metode Support Vector Machine,” Method. J. Tek. Inform. Dan Sist. Inf., vol. 9, no. 2, pp. 23–25, 2023, doi: 10.46880/mtk.v9i2.1887.
[8] C. Paper and R. R. Wahid, “Implementasi Metode Extreme Learning Machine untuk Klasifikasi Tumor Implementasi Metode Extreme Learning Machine untuk Klasifikasi Tumor Otak pada Citra Magnetic Resonance Imaging,” no. May, 2023, doi: 10.33005/santika.v1i0.45.
[9] K. N. Qodri, “Analisis Perbandingan Klasifikasi Tumor Otak Menggunakan Deep Learning,” vol. 1, pp. 1–6, 2024.
[10] A. ResNet5050-, “Klasifikasi Jenis Burung Menggunakan Metode CNN Dan Arsitektur ResNet-50 1,2,” vol. 10, no. 3, pp. 34–46, 2023.
[11] K. Penyakit and P. Daun, “Penerapan metode residual network (ResNet5050) dalam klasifikasi penyakit pada daun gandum,” vol. 07, pp. 58–65, 2022.
[12] S. S. Han et al., “Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis : Automatic construction of onychomycosis datasets by region-based convolutional deep neural network,” vol. 1111111111, pp. 1–14, 2018.
[13] Z. Tang and M. Li, “Mapping Tea Plantations from VHR Images Using OBIA and Convolutional Neural Networks,” no. September, 2020, doi: 10.3390/rs12182935.
[14] J. Imam and B. No, “Klasifikasi Terumbu Karang Menggunakan CNN,” pp. 326–332, 2024.
[15] N. Nova, A. Mulyanti, C. Silmy, A. Putri, and L. Mulyani, “Systematic Review : Pemanfaatan Deep Learning untuk Diagnosis Penyakit Menggunakan MRI,” vol. 5, no. 2, pp. 839–852, 2025.
[16] S. A. Hasanah, A. A. Pravitasari, A. S. Abdullah, and I. N. Yulita, “Applied Sciences A Deep Learning Review of ResNet5050 Architecture for Lung Disease Identification in CXR Image,” 2023.
[17] S. Ahuja, B. Ketan, P. Nilanjan, D. Venkatesan, R. Tapan, and K. Gandhi, “Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices,” vol. 1, pp. 571–585, 2021.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Journal of Intelligent Systems Technology and Informatics
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
License:
This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
