Khalid Jamil¹, Wahab Khan¹, Bilal Khan², Sarwar Shah Khan^2,*

Digital Engineering and Digital Twin, Vol.3, pp. 1-16, 2025, DOI:10.32604/dedt.2025.058943 - 28 February 2025

Abstract Brain tumors are one of the deadliest cancers, partly because they’re often difficult to detect early or with precision. Standard Magnetic Resonance Imaging (MRI) imaging, though essential, has limitations, it can miss subtle or early-stage tumors, which delays diagnosis and affects patient outcomes. This study aims to tackle these challenges by exploring how machine learning (ML) can improve the accuracy of brain tumor identification from MRI scans. Motivated by the potential for artificial intillegence (AI) to boost diagnostic accuracy where traditional methods fall short, we tested several ML models, with a focus on the K-Nearest More >

Xuemei Yang¹, Yuting Zhou², Shiqi Liu¹, Junping Yin^2,3,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3281-3307, 2025, DOI:10.32604/cmc.2024.057991 - 17 February 2025

Abstract Aiming at the problems of information loss and the relationship between features and target tasks in multimodal medical image segmentation, a multimodal medical image segmentation algorithm based on feature decoupling and information bottleneck theory is proposed in this paper. Based on the reversible network, the bottom-up learning method for different modal information is constructed, which enhances the features’ expression ability and the network’s learning ability. The feature fusion module is designed to balance multi-directional information flow. To retain the information relevant to the target task to the maximum extent and suppress the information irrelevant to… More >

Mudassir Khalil¹, Muhammad Imran Sharif^2,*, Ahmed Naeem³, Muhammad Umar Chaudhry¹, Hafiz Tayyab Rauf^4,*, Adham E. Ragab⁵

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1461-1461, 2025, DOI:10.32604/cmc.2024.061589 - 03 January 2025

Abstract This article has no abstract. More >

SOROUSH SOLEYMANI¹, MOHAMMAD DOROUDIAN^2,*, MAHDIEH SOEZI^3,4, ALI BELADI⁵, KIARASH ASGARI², ASO MOBARAKSHAHI², ARYANA AGHAEIPOUR², RONAN MACLOUGHLIN^6,7,8,*

Oncology Research, Vol.33, No.1, pp. 15-26, 2025, DOI:10.32604/or.2024.053069 - 20 December 2024

Abstract Brain metastasis and primary glioblastoma multiforme represent the most common and lethal malignant brain tumors. Its median survival time is typically less than a year after diagnosis. One of the major challenges in treating these cancers is the efficiency of the transport of drugs to the central nervous system. The blood-brain barrier is cooperating with advanced stages of malignancy. The blood-brain barrier poses a significant challenge to delivering systemic medications to brain tumors. Nanodrug delivery systems have emerged as promising tools for effectively crossing this barrier. Additionally, the development of smart nanoparticles brings new hope More >

ZAREMA GILAZIEVA¹, DANIIL MOLDAVSKII¹, EKATERINA LUZINA¹, AISYLU KADYROVA¹, ALISA SHAIMARDANOVA¹, SHAZA ISSA², ALBERT RIZVANOV^1,3,*, VALERIYA SOLOVYEVA¹

BIOCELL, Vol.48, No.12, pp. 1667-1681, 2024, DOI:10.32604/biocell.2024.058490 - 30 December 2024

Abstract Aggressive malignant brain tumors have a poor prognosis, and early detection can significantly improve treatment effectiveness and increase patient survival rates. Various methods are available for diagnosing brain tumors, with biopsy being one of the primary options. However, a biopsy is an invasive procedure that carries a risk of brain damage, highlighting the need for safer alternatives. One promising non-invasive method is liquid biopsy, which involves extracting extracellular vesicles (EVs) from different biological fluids. Most cell types can produce and release extracellular vesicles. EVs isolated from bodily fluids, along with the molecules they carry—such More >

Mahesh Thyluru Ramakrishna¹, Kuppusamy Pothanaicker², Padma Selvaraj³, Surbhi Bhatia Khan^4,7,*, Vinoth Kumar Venkatesan⁵, Saeed Alzahrani⁶, Mohammad Alojail⁶

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 867-883, 2024, DOI:10.32604/cmc.2024.053563 - 15 October 2024

Abstract Brain tumor is a global issue due to which several people suffer, and its early diagnosis can help in the treatment in a more efficient manner. Identifying different types of brain tumors, including gliomas, meningiomas, pituitary tumors, as well as confirming the absence of tumors, poses a significant challenge using MRI images. Current approaches predominantly rely on traditional machine learning and basic deep learning methods for image classification. These methods often rely on manual feature extraction and basic convolutional neural networks (CNNs). The limitations include inadequate accuracy, poor generalization of new data, and limited ability… More >

Md. Atiqur Rahman¹, Mustavi Ibne Masum¹, Khan Md Hasib², M. F. Mridha^3,*, Sultan Alfarhood⁴, Mejdl Safran^4,*, Dunren Che⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2425-2448, 2024, DOI:10.32604/cmes.2024.050760 - 08 July 2024

Abstract Brain tumors pose a significant threat to human lives and have gained increasing attention as the tenth leading cause of global mortality. This study addresses the pressing issue of brain tumor classification using Magnetic resonance imaging (MRI). It focuses on distinguishing between Low-Grade Gliomas (LGG) and High-Grade Gliomas (HGG). LGGs are benign and typically manageable with surgical resection, while HGGs are malignant and more aggressive. The research introduces an innovative custom convolutional neural network (CNN) model, Glioma-CNN. GliomaCNN stands out as a lightweight CNN model compared to its predecessors. The research utilized the BraTS 2020 More >

Yuting Zhou¹, Xuemei Yang¹, Junping Yin^2,3,4,*, Shiqi Liu¹

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5313-5333, 2024, DOI:10.32604/cmc.2024.052060 - 20 June 2024

Abstract Gliomas have the highest mortality rate of all brain tumors. Correctly classifying the glioma risk period can help doctors make reasonable treatment plans and improve patients’ survival rates. This paper proposes a hierarchical multi-scale attention feature fusion medical image classification network (HMAC-Net), which effectively combines global features and local features. The network framework consists of three parallel layers: The global feature extraction layer, the local feature extraction layer, and the multi-scale feature fusion layer. A linear sparse attention mechanism is designed in the global feature extraction layer to reduce information redundancy. In the local feature… More >

Zhenyu Qian¹, Yizhang Jiang¹, Zhou Hong¹, Lijun Huang², Fengda Li³, KhinWee Lai⁶, Kaijian Xia^4,5,6,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4741-4762, 2024, DOI:10.32604/cmc.2024.050920 - 20 June 2024

Abstract In this paper, we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering (MAS-DSC) algorithm, aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data, particularly in the field of medical imaging. Traditional deep subspace clustering algorithms, which are mostly unsupervised, are limited in their ability to effectively utilize the inherent prior knowledge in medical images. Our MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process, thereby enhancing the discriminative power of the feature representations. Additionally, the multi-scale feature extraction… More > Graphic Abstract

Samar M. Alqhtani¹, Toufique A. Soomro^2,*, Faisal Bin Ubaid³, Ahmed Ali⁴, Muhammad Irfan⁵, Abdullah A. Asiri⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1539-1562, 2024, DOI:10.32604/cmes.2024.051475 - 20 May 2024

Abstract Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various countries. Magnetic resonance imaging (MRI) and computed tomography (CT) are utilized to capture brain images. MRI plays a crucial role in the diagnosis of brain tumors and the examination of other brain disorders. Typically, manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely intervention. However, early diagnosis of brain tumors is intricate, necessitating the use of computerized methods. This research introduces an innovative approach for… More > Graphic Abstract