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 >

Suganya Athisayamani¹, A. Robert Singh², Gyanendra Prasad Joshi³, Woong Cho^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 155-183, 2025, DOI:10.32604/cmes.2024.056129 - 17 December 2024

Abstract In radiology, magnetic resonance imaging (MRI) is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures. MRI is particularly effective for detecting soft tissue anomalies. Traditionally, radiologists manually interpret these images, which can be labor-intensive and time-consuming due to the vast amount of data. To address this challenge, machine learning, and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI scans. This manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning methods. There are three… 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 >

James Wysock^1,*, Jesse Persily^1,*, Angela Tong², Eli Rapoport¹, Ben Zaslavsky¹, Majlinda Tafa¹, Herbert Lepor¹

Canadian Journal of Urology, Vol.31, No.5, pp. 11986-11991, 2024

Abstract Introduction: Local disease recurrence following focal therapy (FT) for prostate cancer may be due to failure to eradicate focal disease or development of disease in the untreated prostate (in- and out-of-field recurrences). Several studies suggest in-field contrast enhancement (CE) on post treatment multi-parametric (mp) MRI between 6-12 months following FT indicates residual disease. The present study assesses the incidence and oncologic implications of early CE observed following primary partial gland cryoablation (PPGCA).
Material and methods: The surveillance protocol for men enrolled in our prospective outcomes study following PPGCA included mpMRI at 6-12 months, 2 years, 3.5 years,… More >

Novsheena Rasool^1,*, Javaid Iqbal Bhat¹, Najib Ben Aoun^2,3, Abdullah Alharthi⁴, Niyaz Ahmad Wani⁵, Vikram Chopra⁶, Muhammad Shahid Anwar^7,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 885-909, 2024, DOI:10.32604/cmc.2024.055900 - 15 October 2024

Abstract Gliomas are aggressive brain tumors known for their heterogeneity, unclear borders, and diverse locations on Magnetic Resonance Imaging (MRI) scans. These factors present significant challenges for MRI-based segmentation, a crucial step for effective treatment planning and monitoring of glioma progression. This study proposes a novel deep learning framework, ResNet Multi-Head Attention U-Net (ResMHA-Net), to address these challenges and enhance glioma segmentation accuracy. ResMHA-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention mechanisms. This powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture… 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 >

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 >

Fadl Dahan^*

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 381-395, 2024, DOI:10.32604/iasc.2024.047921 - 21 May 2024

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 >

Sharaf J. Malebary^*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1301-1317, 2024, DOI:10.32604/cmc.2024.047917 - 25 April 2024

Abstract Brain tumors are a pressing public health concern, characterized by their high mortality and morbidity rates. Nevertheless, the manual segmentation of brain tumors remains a laborious and error-prone task, necessitating the development of more precise and efficient methodologies. To address this formidable challenge, we propose an advanced approach for segmenting brain tumor Magnetic Resonance Imaging (MRI) images that harnesses the formidable capabilities of deep learning and convolutional neural networks (CNNs). While CNN-based methods have displayed promise in the realm of brain tumor segmentation, the intricate nature of these tumors, marked by irregular shapes, varying sizes,… More >