B. Rakesh Babu; Vullanki Rajesh

Automated Brain Tumor Detection Using Hybrid CNN - Models from T1 – weighted MRI Scans

PDF (929KB), PP.92-103

Views: 0 Downloads: 0

Author(s)

1. Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2026.03.06

Received: 5 Jan. 2026 / Revised: 16 Mar. 2026 / Accepted: 12 Apr. 2026 / Published: 8 Jun. 2026

Index Terms

Brain Tumour, T1-Weighted, Convolutional Neural Network, Artificial Neural Network

Abstract

Detecting and classifying brain tumours is essential for early diagnosis and effective treatment planning, significantly enhancing patient outcomes. This research presents a deep learning-based approach that utilizes T1-weighted MRI data to automatically identify and classify brain tumours, distinguishing between normal and abnormal cases. The proposed methodology consists of four key steps: pre-processing, segmentation, feature extraction, and classification. In the pre-processing stage, image quality is enhanced using Contrast Limited Adaptive Histogram Equalization (CLAHE) to boost contrast, along with a Gaussian filter to reduce noise. Tumour segmentation is achieved through thresholding, which effectively isolates the tumour regions. For feature extraction, a Convolutional Neural Network (CNN) captures high-dimensional features that are vital for classification. To accurately differentiate between normal and abnormal tumours, an Artificial Neural Network (ANN) is employed for classification. The effectiveness of the proposed technique is evaluated based on performance metrics such as time, accuracy, and peak signal-to-noise ratio (PSNR). The obtained parameters are compared with existing techniques to highlight improvements in detection and classification performance. Among the tested images, the best result achieved a PSNR of 13.015 dB, an accuracy of 99.231%, and a computational time of 1.267 ms, demonstrating the efficiency and reliability of the proposed method for brain tumor detection and classification. Overall, this approach provides an effective and automated method for detecting brain tumours, aiding in clinical decision-making and diagnosis.

Cite This Paper

B. Rakesh Babu, Vullanki Rajesh, "Automated Brain Tumor Detection Using Hybrid CNN - Models from T1 – weighted MRI Scans", International Journal of Intelligent Systems and Applications(IJISA), Vol.18, No.3, pp.92-103, 2026. DOI:10.5815/ijisa.2026.03.06

Reference

[1]E. Schulz, S.J. Gershman, "The Algorithmic Architecture of Exploration in the Human Brain", Current Opinion in Neurobiology, Vol.55, pp.7-14, 2019. DOI: 10.1016/j.conb.2018.11.002.
[2]S.L. Fernandes, U.J. Tanik, V. Rajinikanth, K.A. Karthik, "A Reliable Framework for Accurate Brain Image Examination and Treatment Planning Based on Early Diagnosis Support for Clinicians", Neural Computing and Applications, Vol.32, No.20, pp.15897-15908, 2020. DOI:10.1007/s00521-019-04194-5.
[3]C. Kv, G.G. King, "Brain Tumour Classification: A Comprehensive Systematic Review on Various Constraints", Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, pp.1-13, 2022. DOI:10.1080/21681163.2022
[4]A. Ismael, A.M. Abdel-Qader, "Brain Tumor Classification Using Deep Learning Networks", IEEE Access, Vol.8, pp.202190-202198, 2020. DOI:10.1109/ACCESS.2020.3034511.
[5]M. Litjens et al., "A Survey on Deep Learning in Medical Image Analysis", Medical Image Analysis, Vol.42, pp.60-88, 2017. DOI:10.1016/j.media.2017.07.005.
[6]R.M. Haralick, K. Shanmugam, I. Dinstein, "Textural Features for Image Classification", IEEE Transactions on Systems, Man, and Cybernetics, Vol.3, No.6, pp.610-621, 1973. DOI:10.1109/TSMC.1973.4309314.
[7]J. Long, E. Shelhamer, T. Darrell, "Fully Convolutional Networks for Semantic Segmentation", Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.3431-3440, 2015. DOI:10.1109/CVPR.2015.7298965.
[8]M. Aamir, S. Ali, "Hyperparametric Convolutional Neural Network for Brain Tumor Detection", Diagnostics, Vol.14, No.16, pp.1714, 2024. DOI:10.3390/diagnostics14161714.
[9]N. Asiri, M. Hussain, F. Al Adel, N. Alzaidi, "Deep Learning Based Computer-Aided Diagnosis Systems for Breast Cancer in Mammograms: A Survey", IEEE Access, Vol.8, pp.220990-221012, 2024. DOI:10.1109/ACCESS.2020.3042115.
[10]M.S. Miah, M.M. Rahman, "Brain Tumor Detection Using Convolutional Neural Networks", International Journal of Advanced Computer Science and Applications, Vol.14, No.1, pp.2023-2030, 2023
[11]M.S.I. Shawon, M.M. Islam, "A Cost-Sensitive Deep Neural Network for Brain Tumor Detection", arXiv Preprint, pp.1-10, 2023. DOI:10.48550/arXiv.2308.00608.
[12]G. Wang, W. Li, "An Improved Deep Learning Approach for Brain Tumor Classification", IEEE Access, Vol.10, pp.2033-2042, 2022. DOI:10.1109/ACCESS.2022
[13]J. Chen, S. Liu, "Brain Tumor Detection and Classification via Deep Learning", IEEE Access, Vol.10, pp.2022-2032, 2022.
[14]M.Z. Islam, M.M. Islam, A. Asraf, "A Combined Deep CNN-LSTM Network for the Detection of Novel Coronavirus (COVID-19) Using X-ray Images", Informatics in Medicine Unlocked, Vol.20, pp.100412, 2021. DOI:10.1016/j.imu.2020.100412.
[15]A. Biswas, M.K. Islam, "Brain Tumor Types Classification using K-means Clustering and ANN Approach", arXiv Preprint, pp.1-8, 2021. DOI:10.48550/arXiv.2104.00980.
[16]Y. Li, L. Shen, "Deep Learning Based Classification of Brain Tumors with Adversarial Domain Adaptation", IEEE Access, Vol.8, pp.55145-55155, 2020. DOI:10.1109/ACCESS.2020.2981766.
[17]Y. Zhang, B. Zhang, Q. Zhu, "Brain Tumor Segmentation Using a Hybrid Clustering and Convolutional Neural Network Approach", IEEE Access, Vol.8, pp.61023-61033, 2020.
[18]S. Saxena, R. Vijayvargiya, "Deep Learning Approach for Brain Tumor Prediction Using MRI Images", arXiv Preprint, pp.1-7, 2019. DOI:10.48550/arXiv.1911.02265.
[19]K. Patel, R. Doshi, "A Study on Brain Tumor Detection Using Deep Learning Techniques", International Journal of Engineering and Advanced Technology, Vol.8, No.6, pp.1239-1242, 2019.
[20]J. Cheng, W. Huang, S. Cao, R. Yang, W. Yang, Z. Yun, Z. Wang, Q. Feng, "Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition", IEEE Journal of Biomedical and Health Informatics, Vol.22, No.5, pp.1489-1499, 2018. DOI:10.1109/JBHI.2017.2777480.
[21]M.S. Hossain, G. Muhammad, "Brain Tumor Detection Using Multimodal Deep Learning", IEEE Journal of Translational Engineering in Health and Medicine, Vol.6, pp.1-11, 2018. DOI:10.1109/JTEHM.2018.2871378.
[22]M.K. Hasan, M. Biswas, M.M. Ahmed, B.K. Paul, "Fusion of Handcrafted and Deep-Learning Features for Brain Tumor Classification", SN Computer Science, Vol.5, No.1, pp.34, 2024. DOI:10.1007/s42979-023-02345-5.
[23]C. Venkatesh, P. Bojja, "A Dynamic Optimization and Deep Learning Technique for Detection of Lung Cancer in CT Images and Data Access Through Internet of Things", Wireless Personal Communications, Vol.125, pp.2621-2646, 2022. DOI:10.1007/s11277-022-09604-7.
[24]C. Venkatesh, J. Chinna Babu, A. Kiran, C.H. Nagaraju, M. Kumar, "A Hybrid Model for Lung Cancer Prediction Using Patch Processing and Deep Learning on CT Images", Multimedia Tools and Applications, Vol.83, pp.43931-43952, 2024. DOI:10.1007/s11042-023-16033-0.
[25]S.R. Niakan Kalhori, S. Mohseni, F. Niakan Kalhori, "MRI-based Brain Tumor Detection Using Convolutional Deep Learning and Machine Learning Algorithms", BMC Medical Informatics and Decision Making, Vol.23, No.1, pp.16, 2023. DOI:10.1186/s12911-023-02065-2.
[26]M. Islam, M.R. Haque, M.A. Iqbal, M.K. Hasan, "Brain Tumor Detection with GLCM Feature Extraction and Hybrid Classification Techniques", Proceedings of the International Conference on Intelligent Systems, Data Science and Computing, pp.45-54, 2022. DOI:10.1007/978-981-16-8517-0_5.
[27]T.L. Narayana, C. Venkatesh, A. Kiran, C.B.J., A. Kumar, S.B. Khan, A. Almusharraf, M.T. Quasim, "Advances in Real-Time Smart Monitoring of Environmental Parameters Using IoT and Sensors", Heliyon, Vol.10, 2024.
[28]C. Venkatesh et al., "An Automatic Diagnostic Model for the Detection and Classification of Cardiovascular Diseases Based on Swarm Intelligence Technique", Heliyon, Vol.10, 2024.
[29]A.K. Sharma, A. Nandal, A. Dhaka, K. Polat, M. Nour, F. Alenezi, A. Alharbi, "HOG Transformation-Based Feature Extraction Framework in Modified ResNet50 Model for Brain Tumor Detection", Biomedical Signal Processing and Control, Vol.84, pp.104737, 2023. DOI:10.1016/j.bspc.2023.104737.
[30]Nanda Prakash et al., "Real-Time Liver Tumor Detection with a Multi-Class Ensemble Deep Learning Framework", Engineering, Technology & Applied Science Research, Vol.14, pp.2024.
[31]B. Maas, E. Zabeh, S. Arabshahi, "Quick Tumor Net: Fast Automatic Multi-Class Segmentation of Brain Tumors", arXiv Preprint, pp.1-10, 2020. DOI:10.48550/arXiv.2012.12410.
[32]D. Filatov, G.N.A.H. Yar, "Brain Tumor Diagnosis and Classification via Pre-Trained Convolutional Neural Networks", arXiv Preprint, pp.1-12, 2022. DOI:10.48550/arXiv.2208.00768.
[33]M. Güler, E. Namlı, "Brain Tumor Detection with Deep Learning Methods’ Classifier Optimization Using Medical Images", Applied Sciences, Vol.14, No.2, pp.642, 2024. DOI:10.3390/app14020642.
[34]A.K. Sharma, R.K. Nandal, "Comparison of F-Measure, BER, and PSNR of Tumor Detection Using Hybridization of Fuzzy and Region Growing", International Journal of Computer Applications, Vol.124, No.3, pp.34-39, 2015.
[35]F. Isensee et al., "A Systematic Approach for MRI Brain Tumor Localization and Segmentation Using Deep Learning and Active Contouring", arXiv Preprint, pp.1-15, 2021. DOI:10.48550/arXiv.2102.03532.
[36]P. Podder, S. Bharati, M.A. Rahman, U. Kose, "Transfer Learning for Classification of Brain Tumor", Advancements in Applied Metaheuristic Computing, CRC Press, pp.199-218, 2021.

International Journal of Intelligent Systems and Applications (IJISA)