Seyong Jin¹, Muhammad Fayaz², L. Minh Dang³, Hyoung-Kyu Song³, Hyeonjoon Moon^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.070667 - 10 November 2025

Abstract Brain tumors require precise segmentation for diagnosis and treatment plans due to their complex morphology and heterogeneous characteristics. While MRI-based automatic brain tumor segmentation technology reduces the burden on medical staff and provides quantitative information, existing methodologies and recent models still struggle to accurately capture and classify the fine boundaries and diverse morphologies of tumors. In order to address these challenges and maximize the performance of brain tumor segmentation, this research introduces a novel SwinUNETR-based model by integrating a new decoder block, the Hierarchical Channel-wise Attention Decoder (HCAD), into a powerful SwinUNETR encoder. The HCAD… More >

Ameer Hamza, Robertas Damaševičius^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-41, 2026, DOI:10.32604/cmc.2025.069721 - 10 November 2025

Abstract This systematic review aims to comprehensively examine and compare deep learning methods for brain tumor segmentation and classification using MRI and other imaging modalities, focusing on recent trends from 2022 to 2025. The primary objective is to evaluate methodological advancements, model performance, dataset usage, and existing challenges in developing clinically robust AI systems. We included peer-reviewed journal articles and high-impact conference papers published between 2022 and 2025, written in English, that proposed or evaluated deep learning methods for brain tumor segmentation and/or classification. Excluded were non-open-access publications, books, and non-English articles. A structured search was… More >

Sarfaraz Abdul Sattar Natha^1,*, Mohammad Siraj^2,*, Majid Altamimi², Adamali Shah², Maqsood Mahmud³

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4179-4201, 2025, DOI:10.32604/cmes.2025.072529 - 23 December 2025

Abstract A brain tumor is a disease in which abnormal cells form a tumor in the brain. They are rare and can take many forms, making them difficult to treat, and the survival rate of affected patients is low. Magnetic resonance imaging (MRI) is a crucial tool for diagnosing and localizing brain tumors. However, the manual interpretation of MRI images is tedious and prone to error. As artificial intelligence advances rapidly, DL techniques are increasingly used in medical imaging to accurately detect and diagnose brain tumors. In this study, we introduce a deep convolutional neural network… More >

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

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2457-2479, 2025, DOI:10.32604/cmes.2025.072765 - 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… More >

Sudhakar Tummala^1,2,*, Sajjad Hussain Chauhdary³, Vikash Singh⁴, Roshan Kumar⁵, Seifedine Kadry⁶, Jungeun Kim^7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3585-3605, 2025, DOI:10.32604/cmes.2025.069731 - 30 September 2025

Abstract Lightweight deep learning models are increasingly required in resource-constrained environments such as mobile devices and the Internet of Medical Things (IoMT). Multi-head convolution with channel attention can facilitate learning activations relevant to different kernel sizes within a multi-head convolutional layer. Therefore, this study investigates the capability of novel lightweight models incorporating residual multi-head convolution with channel attention (ResMHCNN) blocks to classify medical images. We introduced three novel lightweight deep learning models (BT-Net, LCC-Net, and BC-Net) utilizing the ResMHCNN block as their backbone. These models were cross-validated and tested on three publicly available medical image datasets:… More >

Rosa Scarpitta^1,#, Emiliano Cappello^1,#, Alice Cangialosi¹, Veronica Gori¹, Giulia De Luca^1,2, Giovanni Gori³, Guido Bocci^1,*

Oncology Research, Vol.33, No.10, pp. 2603-2656, 2025, DOI:10.32604/or.2025.066260 - 26 September 2025

Abstract Central nervous system (CNS) tumors are the most common solid tumors in pediatric patients and the leading cause of childhood cancer-related mortality. Their rarity compared to adult cancers has made enrolling sufficient cases for clinical trials challenging. Consequently, pediatric CNS tumors were long treated with adult protocols despite distinct biological and clinical characteristics. This review examines key aspects of phase I pediatric oncology trials, including study design, primary outcomes, and pharmacological approaches, along with secondary considerations like clinical responses and ethical aspects. Firstly, we evaluated all phase I trial protocols focusing on pediatric CNS tumors… More > Graphic Abstract

Khalil Ibrahim Lairedj¹, Zouaoui Chama¹, Amina Bagdaoui¹, Samia Larguech², Younes Menni^3,4,*, Nidhal Becheikh⁵, Lioua Kolsi^6,*, Badr M. Alshammari⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2419-2443, 2025, DOI:10.32604/cmes.2025.069396 - 31 August 2025

Abstract Brain tumor segmentation from Magnetic Resonance Imaging (MRI) supports neurologists and radiologists in analyzing tumors and developing personalized treatment plans, making it a crucial yet challenging task. Supervised models such as 3D U-Net perform well in this domain, but their accuracy significantly improves with appropriate preprocessing. This paper demonstrates the effectiveness of preprocessing in brain tumor segmentation by applying a pre-segmentation step based on the Generalized Gaussian Mixture Model (GGMM) to T1 contrast-enhanced MRI scans from the BraTS 2020 dataset. The Expectation-Maximization (EM) algorithm is employed to estimate parameters for four tissue classes, generating a More >

Fatma M. Talaat^1,2,*, Mohamed Salem¹, Mohamed Shehata^3,4,*, Warda M. Shaban⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2325-2358, 2025, DOI:10.32604/cmes.2025.067195 - 31 August 2025

Abstract The diagnosis of brain tumors is an extended process that significantly depends on the expertise and skills of radiologists. The rise in patient numbers has substantially elevated the data processing volume, making conventional methods both costly and inefficient. Recently, Artificial Intelligence (AI) has gained prominence for developing automated systems that can accurately diagnose or segment brain tumors in a shorter time frame. Many researchers have examined various algorithms that provide both speed and accuracy in detecting and classifying brain tumors. This paper proposes a new model based on AI, called the Brain Tumor Detection (BTD)… More >

Li Li, Xiao Wang^*, Ran Ding, Linlin Luo, Qinmu Wu, Zhiqin He

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1287-1309, 2025, DOI:10.32604/cmc.2025.065670 - 29 August 2025

Abstract Brain tumors are neoplastic diseases caused by the proliferation of abnormal cells in brain tissues, and their appearance may lead to a series of complex symptoms. However, current methods struggle to capture deeper brain tumor image feature information due to the variations in brain tumor morphology, size, and complex background, resulting in low detection accuracy, high rate of misdiagnosis and underdiagnosis, and challenges in meeting clinical needs. Therefore, this paper proposes the CMS-YOLO network model for multi-category brain tumor detection, which is based on the You Only Look Once version 10 (YOLOv10s) algorithm. This model… More >

Yao-Tien Chen¹, Nisar Ahmad^1,*, Khursheed Aurangzeb²

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1197-1224, 2025, DOI:10.32604/cmes.2025.066580 - 31 July 2025

Abstract Accurate and efficient brain tumor segmentation is essential for early diagnosis, treatment planning, and clinical decision-making. However, the complex structure of brain anatomy and the heterogeneous nature of tumors present significant challenges for precise anomaly detection. While U-Net-based architectures have demonstrated strong performance in medical image segmentation, there remains room for improvement in feature extraction and localization accuracy. In this study, we propose a novel hybrid model designed to enhance 3D brain tumor segmentation. The architecture incorporates a 3D ResNet encoder known for mitigating the vanishing gradient problem and a 3D U-Net decoder. Additionally, to… More > Graphic Abstract