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 >

Lakshmi S V V¹, Shiloah Elizabeth Darmanayagam^1,*, Sunil Retmin Raj Cyril²

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 733-757, 2025, DOI:10.32604/cmes.2024.056424 - 17 December 2024

Abstract Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people everywhere. Due to its ability to produce a detailed view of the soft tissues, including the spinal cord, nerves, intervertebral discs, and vertebrae, Magnetic Resonance Imaging is thought to be the most effective method for imaging the spine. The semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar diseases. It is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of… More >

Noam Bar-Yaakov^1,2, Ziv Savin^1,2, Ibrahim Fahoum^2,3, Sophie Barnes^2,4, Yuval Bar-Yosef^1,2, Ofer Yossepowitch^1,2, Gal Keren-Paz^1,2, Roy Mano^1,2

Canadian Journal of Urology, Vol.31, No.1, pp. 11793-11801, 2024

Abstract Introduction: Prostate cancer screening with PSA is associated with low specificity; furthermore, little is known about the optimal timing of biopsy. We aimed to evaluate whether a risk classification system combining PSA density (PSAD) and mpMRI can predict clinically significant cancer and determine biopsy timing.
Materials and methods: We reviewed the medical records of 256 men with a PI-RADS ≥ 3 lesion on mpMRI who underwent transperineal targeted and systematic biopsies of the prostate between 2017-2019. Patients were stratified into three risk groups based on PSAD and mpMRI findings.
The study endpoint was clinically significant prostate cancer (CSPC).… More >

Snir Dekalo^1,2, Ohad Mazliah², Eyal Barkai^1,2, Yuval Bar-Yosef^1,2, Haim Herzberg^1,2, Tomer Bashi^1,2, Ibrahim Fahoum^2,3, Sophie Barnes^2,4, Mario Sofer^1,2, Ofer Yossepowitch^1,2, Gal Keren-Paz^1,2, Roy Mano^1,2

Canadian Journal of Urology, Vol.31, No.4, pp. 11955-11962, 2024

Abstract Introduction: Most men diagnosed with very-low and low-risk prostate cancer are candidates for active surveillance; however, there is still a misclassification risk. We examined whether PI-RADS category 4 or 5 combined with ISUP 1 on prostate biopsy predicts upgrading and/ or adverse pathology at radical prostatectomy.
Materials and methods: A total of 127 patients had ISUP 1 cancer on biopsy after multiparametric MRI (mpMRI) and then underwent radical prostatectomy. We then evaluated them for ISUP upgrading and/or adverse pathology on radical prostatectomy.
Results: Eight-nine patients (70%) were diagnosed with PI-RADS 4 or 5 lesions. ISUP upgrading was significantly… More >

Oncology Research Editorial Office

Oncology Research, Vol.32, No.11, pp. 1829-1829, 2024, DOI:10.32604/or.2024.056904 - 16 October 2024

Abstract This article has no abstract. More >

Deema Mohammed Alsekait¹, Ahmed Younes Shdefat², Ayman Nabil³, Asif Nawaz^4,*, Muhammad Rizwan Rashid Rana⁴, Zohair Ahmed⁵, Hanaa Fathi⁶, Diaa Salama AbdElminaam^6,7,8

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3967-3990, 2024, DOI:10.32604/cmc.2024.054591 - 12 September 2024

Abstract Heart disease remains a leading cause of morbidity and mortality worldwide, highlighting the need for improved diagnostic methods. Traditional diagnostics face limitations such as reliance on single-modality data and vulnerability to apparatus faults, which can reduce accuracy, especially with poor-quality images. Additionally, these methods often require significant time and expertise, making them less accessible in resource-limited settings. Emerging technologies like artificial intelligence and machine learning offer promising solutions by integrating multi-modality data and enhancing diagnostic precision, ultimately improving patient outcomes and reducing healthcare costs. This study introduces Heart-Net, a multi-modal deep learning framework designed to… More >

Bing Li^1,2,*, Liangyu Wang¹, Xia Liu^1,2, Hongbin Fan¹, Bo Wang³, Shoudi Tong¹

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1543-1561, 2024, DOI:10.32604/cmc.2024.052009 - 18 July 2024

Abstract Nuclear magnetic resonance imaging of breasts often presents complex backgrounds. Breast tumors exhibit varying sizes, uneven intensity, and indistinct boundaries. These characteristics can lead to challenges such as low accuracy and incorrect segmentation during tumor segmentation. Thus, we propose a two-stage breast tumor segmentation method leveraging multi-scale features and boundary attention mechanisms. Initially, the breast region of interest is extracted to isolate the breast area from surrounding tissues and organs. Subsequently, we devise a fusion network incorporating multi-scale features and boundary attention mechanisms for breast tumor segmentation. We incorporate multi-scale parallel dilated convolution modules into… More >

Mian Muhammad Danyal^1,2, Sarwar Shah Khan^3,4,*, Rahim Shah Khan⁵, Saifullah Jan², Naeem ur Rahman⁶

Journal of Intelligent Medicine and Healthcare, Vol.2, pp. 35-53, 2024, DOI:10.32604/jimh.2024.051340 - 08 July 2024

Abstract Multi-modality medical images are essential in healthcare as they provide valuable insights for disease diagnosis and treatment. To harness the complementary data provided by various modalities, these images are amalgamated to create a single, more informative image. This fusion process enhances the overall quality and comprehensiveness of the medical imagery, aiding healthcare professionals in making accurate diagnoses and informed treatment decisions. In this study, we propose a new hybrid pre-processing approach, Laplacian Filter + Discrete Fourier Transform (LF+DFT), to enhance medical images before fusion. The LF+DFT approach highlights key details, captures small information, and sharpens… 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 >

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