A Deep Learning Approach Focusing on Diagnostic Sensitivity to Enhance Clinical Differentiation of Brain Tumors via Sequential ADC-MRI Analysis

Article Sidebar

PDF
Published: Mar 27, 2026
Keywords:
Apparent diffusion coefficient-diffusion weighted imaging (ADC-MRI), brain tumor, clinical sensitivity, convolutional neural networks (CNN), deep learning, diagnostic accuracy, long short-term memory (LSTM)

Main Article Content

Muhammad Fuad Alharis
Banyuwangi State Polytechnic, Banyuwangi, Indonesia
Heni Fatmawati
Jember University, Jember, Indonesia

Abstract

 The differentiation of benign and malignant brain tumors using Apparent Diffusion Coefficient (ADC) MRI scans remains a challenge for clinicians, owing to the high variability of morphological features and the subtle signs of tissue densities. The present study proposes a simple yet highly effective deep learning-based framework for the classification of brain tumors as benign or malignant. The proposed framework incorporates a combination of Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM). The major advantage of the proposed framework is the use of seven consecutive image slices for brain tumor classification, unlike the conventional methods where a single individual image slice is considered. The use of seven consecutive image slices by the proposed framework actually attempts to capture the volumetric features of brain tumors, thus creating a more comprehensive picture of the brain tumor. The accuracy of the proposed framework for brain tumor classification is 98.05%, with a sensitivity of 100%, thus making the framework more reliable for the identification of malignant brain tumors and their safe elimination.

Article Details

Issue
Vol. 1 No. 01 (2026): Foundations of Medicine-Driven Informatics: Clinical Necessity as the Catalyst for Technological Innovation
Section
Articles
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

[1] J. Singh, M. Bhardwaj, A. Pathak, and L. Sharma, “Enhanced brain tumor classification in MRI using an optimized deep random graph dilated diffusion convolutional attention network,” Med. Phys., vol. 52, no. 10, Oct. 2025, doi: 10.1002/mp.70028.

[2] S. Saeedi, S. Rezayi, H. Keshavarz, and S. R. Niakan Kalhori, “MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques,” BMC Med. Inform. Decis. Mak., vol. 23, no. 1, p. 16, Jan. 2023, doi: 10.1186/s12911-023-02114-6.

[3] A. M. Dikande Simo, A. Tchagna Kouanou, V. Monthe, M. Kameni Nana, and B. Moffo Lonla, “Introducing a deep learning method for brain tumor classification using MRI data towards better performance,” Informatics Med. Unlocked, vol. 44, p. 101423, 2024, doi: 10.1016/j.imu.2023.101423.

[4] A. Ali, X. Li, W. K. Mashwani, M. Abiad, F. K. Karim, and S. M. Mostafa, “Multi-class brain tumor MRI segmentation and classification using deep learning and machine learning approaches,” Cancer Imaging, vol. 25, no. 1, p. 131, Nov. 2025, doi: 10.1186/s40644-025-00953-2.

[5] S. M. Vijithananda et al., “Feature extraction from MRI ADC images for brain tumor classification using machine learning techniques,” Biomed. Eng. Online, vol. 21, no. 1, p. 52, Dec. 2022, doi: 10.1186/s12938-022-01022-6.

[6] R. Ranjbarzadeh, A. Bagherian Kasgari, S. Jafarzadeh Ghoushchi, S. Anari, M. Naseri, and M. Bendechache, “Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images,” Sci. Rep., vol. 11, no. 1, p. 10930, May 2021, doi: 10.1038/s41598-021-90428-8.

[7] S. Tabatabaei, K. Rezaee, and M. Zhu, “Attention transformer mechanism and fusion-based deep learning architecture for MRI brain tumor classification system,” Biomed. Signal Process. Control, vol. 86, p. 105119, Sep. 2023, doi: 10.1016/j.bspc.2023.105119.

[8] N.-H. Lu, Y.-H. Huang, K.-Y. Liu, and T.-B. Chen, “Deep learning-driven brain tumor classification and segmentation using non-contrast MRI,” Sci. Rep., vol. 15, no. 1, p. 27831, Jul. 2025, doi: 10.1038/s41598-025-13591-2.

[9] N. Ullah, A. Javed, A. Alhazmi, S. M. Hasnain, A. Tahir, and R. Ashraf, “TumorDetNet: A unified deep learning model for brain tumor detection and classification,” PLoS One, vol. 18, no. 9, p. e0291200, Sep. 2023, doi: 10.1371/journal.pone.0291200.

[10] S. Mehta and D. Kundra, “A Robust CNN-LSTM Framework for Sensitive and Specific Brain Tumor Classifications,” in 2025 International Conference on Automation and Computation (AUTOCOM), IEEE, Mar. 2025, pp. 1622–1626. doi: 10.1109/AUTOCOM64127.2025.10956792.

[11] P. A. Awate, V. K. K. Ravi, S. Bhardwaj, M. M. Hoque, B. Nepal, and G. Kalra, “Hybrid CNN-LSTM Model for Accurate Detection and Classification of Brain Tumors Using MRI Scans,” in 2025 3rd World Conference on Communication & Computing (WCONF), IEEE, Jul. 2025, pp. 1–6. doi: 10.1109/WCONF64849.2025.11233441.

[12] S. Montaha, S. Azam, A. K. M. R. H. Rafid, M. Z. Hasan, A. Karim, and A. Islam, “TimeDistributed-CNN-LSTM: A Hybrid Approach Combining CNN and LSTM to Classify Brain Tumor on 3D MRI Scans Performing Ablation Study,” IEEE Access, vol. 10, pp. 60039–60059, 2022, doi: 10.1109/ACCESS.2022.3179577.

[13] K. U. Durga Maheswari, P. Lakshmi Nandini, N. Uma Tejaswini, P. Mahitha, and T. Venkatakrishnamoorthy, “CNN–LSTM Hybrid Architecture For Robust Brain Tumor Detection And Analysis,” Int. J. Interpret. Enigm. Eng., vol. 02, no. 03, pp. 26–30, 2025, doi: 10.62674/ijiee.2025.v2i03.005.

[14] S. K. Rajeev, M. P. Rajasekaran, K. Ramaraj, G. Vishnuvarthanan, T. Arunprasath, and V. Muneeswaran, “A Hybrid CNN-LSTM Network For Brain Tumor Classification Using Transfer Learning,” in 2023 9th International Conference on Smart Computing and Communications (ICSCC), IEEE, Aug. 2023, pp. 77–82. doi: 10.1109/ICSCC59169.2023.10335082.