Open Access

ARTICLE

Channel-Attention DenseNet with Dilated Convolutions for MRI Brain Tumor Classification

Abdu Salam¹, Mohammad Abrar², Raja Waseem Anwer³, Farhan Amin^4,*, Faizan Ullah⁵, Isabel de la Torre^6,*, Gerardo Mendez Mezquita⁷, Henry Fabian Gongora⁷

1 Department of Computer Science, Abdul Wali Khan University, Mardan, 23200, Pakistan
2 Faculty of Computer Studies, Arab Open University, P.O. Box 1596, Muscat, 122, Oman
3 Department of Computer Science, German University of Technology in Oman, P.O. Box 1816, Muscat, 130, Oman
4 School of Computer Science and Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea
5 Department of Computer Science, Bacha Khan University, Charsadda, 24420, Pakistan
6 Department of Signal Theory and Communications, University of Valladolid, Valladolid, 47002, Spain
7 Department of Project Management, Universidad Internacional Iberoamericana, Campeche, 24560, Mexico

* Corresponding Authors: Farhan Amin. Email: ; Isabel de la Torre. Email:

(This article belongs to the Special Issue: Exploring the Impact of Artificial Intelligence on Healthcare: Insights into Data Management, Integration, and Ethical Considerations)

Computer Modeling in Engineering & Sciences 2025, 145(2), 2457-2479. https://doi.org/10.32604/cmes.2025.072765

Received 03 September 2025; Accepted 14 October 2025; Issue published 26 November 2025

Abstract

Brain tumors pose significant diagnostic challenges due to their diverse types and complex anatomical locations. Due to the increase in precision image-based diagnostic tools, driven by advancements in artificial intelligence (AI) and deep learning, there has been potential to improve diagnostic accuracy, especially with Magnetic Resonance Imaging (MRI). However, traditional state-of-the-art models lack the sensitivity essential for reliable tumor identification and segmentation. Thus, our research aims to enhance brain tumor diagnosis in MRI by proposing an advanced model. The proposed model incorporates dilated convolutions to optimize the brain tumor segmentation and classification. The proposed model is first trained and later evaluated using the BraTS 2020 dataset. In our proposed model preprocessing consists of normalization, noise reduction, and data augmentation to improve model robustness. The attention mechanism and dilated convolutions were introduced to increase the model’s focus on critical regions and capture finer spatial details without compromising image resolution. We have performed experimentation to measure efficiency. For this, we have used various metrics including accuracy, sensitivity, and curve (AUC-ROC). The proposed model achieved a high accuracy of 94%, a sensitivity of 93%, a specificity of 92%, and an AUC-ROC of 0.98, outperforming traditional diagnostic models in brain tumor detection. The proposed model accurately identifies tumor regions, while dilated convolutions enhanced the segmentation accuracy, especially for complex tumor structures. The proposed model demonstrates significant potential for clinical application, providing reliable and precise brain tumor detection in MRI.

---

Keywords

---