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

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 >

Qiqi Feng¹, Zhijian Huang², Yanqiu Wang¹, Bin Wang^1,*

International Journal of Mental Health Promotion, Vol.26, No.6, pp. 417-428, 2024, DOI:10.32604/ijmhp.2024.049010

Abstract Mindfulness-based interventions (MBIs) are emerging non-pharmacological treatments for mild traumatic brain injury (mTBI). In this systematic review, the authors aimed to evaluate the potential efficacy of MBIs to provide recommendations for treating patients with mTBI. We searched of the English literature on MBIs for patients with mTBI as of 01 September, 2023, using the PubMed, Web of Science, PsycINFO, and Scopus databases. One author performed data extraction and quality scoring of the included literature according to the proposed protocol, and another conducted the review. The review was not registered. A total of 11 studies met… 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

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

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

Manal El-Hamamsy^*, Hesham Elwakil^†, Amr S. Saad^†, May A. Shawki^*

Oncology Research, Vol.24, No.6, pp. 521-528, 2016, DOI:10.3727/096504016X14719078133528

Abstract Statins have been reported to have a potential radiosensitizing effect that has not been evaluated in clinical trials. The aim of this study was to evaluate the efficacy and safety of simvastatin in addition to whole-brain radiation therapy (WBRT) in patients with brain metastases (BM). A prospective randomized, controlled, open-label pilot study was conducted on 50 Egyptian patients with BM who were randomly assigned to receive 30-Gy WBRT (control group: 25 patients) or 30 Gy WBRT+ simvastatin 80 mg/day for the WBRT period (simvastatin group: 25 patients). The primary outcome was radiological response at 4… More >

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

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

Ahmed A. Alsheikhy¹, Ahmad S. Azzahrani¹, A. Khuzaim Alzahrani², Tawfeeq Shawly³

Computer Systems Science and Engineering, Vol.48, No.3, pp. 853-853, 2024, DOI:10.32604/csse.2024.051630

Abstract This article has no abstract. More >

Fadl Dahan^*

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 381-395, 2024, DOI:10.32604/iasc.2024.047921

Abstract In the domain of medical imaging, the accurate detection and classification of brain tumors is very important. This study introduces an advanced method for identifying camouflaged brain tumors within images. Our proposed model consists of three steps: Feature extraction, feature fusion, and then classification. The core of this model revolves around a feature extraction framework that combines color-transformed images with deep learning techniques, using the ResNet50 Convolutional Neural Network (CNN) architecture. So the focus is to extract robust feature from MRI images, particularly emphasizing weighted average features extracted from the first convolutional layer renowned for… More >

Camden L. Hebson^1,*, Kyle Bliton², Amr Y. Hammouda¹, Kaitlyn Barr³, W. Hampton Gray⁴, Mohini Gunnett², Waldemar F. Carlo¹

Congenital Heart Disease, Vol.19, No.2, pp. 185-195, 2024, DOI:10.32604/chd.2024.048871

Abstract D-transposition of the great arteries (d-TGA) is surgically repaired with the arterial switch operation (ASO) with excellent results, however short and long-term morbidities still develop including neurocognitive delay. Clinically significant central sleep apnea is uncommon in non-premature infants, but when present indicates immature autonomic control of respiration likely due to a neurologic disorder. We report the unanticipated finding of central sleep apnea in four-term neonates with d-TGA after uncomplicated ASO, with the short-term complication of delayed hospital discharge and long-term concerns regarding this early marker of brain immaturity and its hindrance to normal development. Within More >

Hang Sun, Changsheng Li^*, He Zhang

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2505-2522, 2024, DOI:10.32604/cmc.2024.049898

Abstract Brain–computer interface (BCI) systems, such as the P300 speller, enable patients to express intentions without necessitating extensive training. However, the complexity of operational instructions and the slow pace of character spelling pose challenges for some patients. In this paper, an image segmentation P300 selector based on YOLOv7-mask and DeepSORT is proposed. The proposed system utilizes a camera to capture real-world objects for classification and tracking. By applying predefined stimulation rules and object-specific masks, the proposed system triggers stimuli associated with the objects displayed on the screen, inducing the generation of P300 signals in the patient’s… More >