Saeed Mohammed Alqahtani

• Doctor of Philosophy
• Assistant professor at Najran University

Background: Prostate cancer is a leading cause of cancer-related deaths in men worldwide, making accurate diagnosis critical for effective treatment. Recent advancements in artificial intelligence (AI) and machine learning (ML) have shown promise in improving the diagnostic accuracy of prostate cancer. Objectives: This systematic review aims to eva...

Background Prostate cancer, a significant contributor to male cancer mortality globally, demands improved diagnostic strategies. In Saudi Arabia, where the incidence is expected to double, this study assessed the compliance of multiparametric MRI (mpMRI) practices with Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2) guidelines acr...

Brain tumor classification is essential for clinical diagnosis and treatment planning. Deep learning models have shown great promise in this task, but they are often challenged by the complex and diverse nature of brain tumors. To address this challenge, we propose a novel deep residual and region-based convolutional neural network (CNN) architectu...

The global health issue of prostate cancer (PCa) requires better diagnosis and treatment. Photoacoustic imaging (PAI) may change PCa management. This review examines PAI's principles, diagnostic role, and therapeutic guidance. PAI uses optical light excitation and ultrasonic detection for high-resolution functional and molecular imaging. PAI uses e...

Hyperparameter tuning plays a pivotal role in the accuracy and reliability of convolutional neural network (CNN) models used in brain tumor diagnosis. These hyperparameters exert control over various aspects of the neural network, encompassing feature extraction, spatial resolution, non-linear mapping, convergence speed, and model complexity. We pr...

The accurate detection of brain tumors through medical imaging is paramount for precise diagnoses and effective treatment strategies. In this study, we introduce an innovative and robust methodology that capitalizes on the transformative potential of the Swin Transformer architecture for meticulous brain tumor image classification. Our approach han...

Background: Segmenting tumors in MRI scans is a difficult and time-consuming task for radiologists. This is because tumors come in different shapes, sizes, and textures, making them hard to identify visually. Objective: This study proposes a new method called the enhanced regularized ensemble encoder-decoder network (EREEDN) for more accurate brai...

Neurological and brain-related cancers are one of the main causes of death worldwide. A commonly used tool in diagnosing these conditions is Magnetic Resonance Imaging (MRI), yet the manual evaluation of MRI images by medical experts presents difficulties due to time constraints and variability. This research introduces a novel, two-module computer...

This paper presents a comprehensive study on the classification of brain tumor images using five pre-trained vision transformer (ViT) models, namely R50-ViT-l16, ViT-l16, ViT-l32, ViT-b16, and ViT-b32, employing a fine-tuning approach. The objective of this study is to advance the state-of-the-art in brain tumor classification by harnessing the pow...

Nowadays, brain tumors have become a leading cause of mortality worldwide. The brain cells in the tumor grow abnormally and badly affect the surrounding brain cells. These cells could be either cancerous or non-cancerous types, and their symptoms can vary depending on their location, size, and type. Due to its complex and varying structure, detecti...

Accurate classification of brain tumors is essential for effective medical diagnosis and treatment planning. Traditional approaches rely on convolutional neural networks (CNNs) for tumor detection, but they often suffer from high computational demands due to the large number of parameters. In this paper, we propose a novel approach for brain tumor...

Simple Summary The study provides a predictive model by using clinical factors in selecting men who may benefit from the addition of systematic biopsies with an image fusion targeted approach. The approach is likely to improve the detection of csPCa and avoid unnecessary detection of indolent prostate cancers. Abstract The study was aimed to devel...

An increase or ‘upgrade’ in Gleason Score (GS) in prostate cancer following Transrectal Ultrasound (TRUS) guided biopsies remains a significant challenge to overcome. to evaluate whether MRI has the potential to narrow the discrepancy of histopathological grades between biopsy and radical prostatectomy, three hundred and thirty men treated consecut...

Objectives: To determine the prognostic significance of tissue stiffness measurement using transrectal ultrasound shear wave elastography in predicting biochemical recurrence following radical prostatectomy for clinically localized prostate cancer. Patients and Methods: Eligible male patients with clinically localized prostate cancer and extraperit...