Early diagnosis is playing an important role in preventing progress of the Alzheimer’s disease (AD). This paper proposes to improve prediction AD with a deep 3D Convolutional Neural Network (3D-CNN), which can show generic features capturing biomarkers extracted from brain images, adapt different domain datasets, and accurately classify subjects improved fine-tuning method. The 3D-CNN built upon convolutional autoencoder, pre-trained capture anatomical shape variations structural MRI...

Diabetic retinopathy (DR) is a serious retinal disease and considered as leading cause of blindness in the world. Ophthalmologists use optical coherence tomography (OCT) fundus photography for purpose assessing thickness, structure, addition to detecting edema, hemorrhage, scars. Deep learning models are mainly used analyze OCT or images, extract unique features each stage DR therefore classify images disease. Throughout this paper, deep Convolutional Neural Network (CNN) with 18...

Oil leaks onto water surfaces from big tankers, ships, and pipeline cracks cause considerable damage harm to the marine environment. Synthetic Aperture Radar (SAR) images provide an approximate representation for target scenes, including sea land surfaces, oil spills, look-alikes. Detection segmentation of spills SAR are crucial aid in leak cleanups protecting This paper introduces a two-stage deep-learning framework identification spill occurrences based on highly unbalanced dataset. The...

Abstract The proposed AI-based diagnostic system aims to predict the respiratory support required for COVID-19 patients by analyzing correlation between lesions and level of provided patients. Computed tomography (CT) imaging will be used analyze three levels received patient: Level 0 (minimum support), 1 (non-invasive such as soft oxygen), 2 (invasive mechanical ventilation). begin segmenting from CT images creating an appearance model each lesion using a 2D, rotation-invariant,...

Autism spectrum disorder is a neuro-developmental that affects the social abilities of patients. Yet, gold standard autism diagnosis diagnostic observation schedule (ADOS). In this study, we are implementing computer-aided system utilizes structural MRI (sMRI) and resting-state functional (fMRI) to demonstrate both anatomical abnormalities connectivity have high prediction ability autism. The proposed studies how metrics provide an overall whether subject autistic or not correlated with ADOS...

Diabetic retinopathy (DR) is a disease that forms as complication of diabetes. It particularly dangerous since it often goes unnoticed and can lead to blindness if not detected early. Despite the clear importance urgency such an illness, there no precise system for early detection DR so far. Fortunately, could be achieved using deep learning including convolutional neural networks (CNNs), which gained momentum in field medical imaging due its capability being effectively integrated into...

Abstract This paper introduces a deep-learning based computer-aided diagnostic (CAD) system for the early detection of acute renal transplant rejection. For noninvasive kidney rejection at an stage, proposed CAD is on fusion both imaging markers and clinical biomarkers. The former are derived from diffusion-weighted magnetic resonance (DW-MRI) by estimating apparent diffusion coefficients (ADC) representing perfusion blood water inside transplanted kidney. biomarkers, namely: creatinine...

Wilms' tumor, the most prevalent renal tumor in children, is known for its aggressive prognosis and recurrence. Treatment of multimodal, including surgery, chemotherapy, occasionally, radiation therapy. Preoperative chemotherapy used routinely European studies select indications North American trials. The objective this study was to build a novel computer-aided prediction system preoperative response tumors. A total 63 patients (age range: 6 months-14 years) were included study, after...

In addition to the standard observational assessment for autism spectrum disorder (ASD), recent advancements in neuroimaging and machine learning (ML) suggest a rapid objective alternative using brain imaging. This work presents pipelined framework, functional magnetic resonance imaging (fMRI) that allows not only an accurate ASD diagnosis but also identification of regions contributing decision. The proposed framework includes several processing stages: preprocessing, parcellation, feature...

Polarization imaging can give information about surface shape, and roughness. has been used for shape recovery, but with convex/concave reconstruction ambiguity. In this paper, we present a direct method to recovery using both polarization shading that resolves ambiguity, without the need nonlinear optimization routines. Several experiments on synthetic real datasets are reported evaluate proposed method. The consistently outperforms some well-known methods based alone.

Autism spectrum disorder is a neurodevelopmental characterized by impaired social abilities and communication difficulties. The golden standard for autism diagnosis in research rely on behavioral features, example, the observation schedule, Diagnostic Interview-Revised. In this study we introduce computer-aided system that uses features from structural MRI (sMRI) resting state functional (fMRI) to help predict an clinicians. proposed capable of parcellating brain regions show which areas are...

This study proposes a Computer-Aided Diagnostic (CAD) system to diagnose subjects with autism spectrum disorder (ASD). The CAD identifies morphological anomalies within the brain regions of ASD subjects. Cortical features are scored according their contribution in diagnosing subject be or typically developed (TD) based on trained machine-learning (ML) model. approach opens hope for developing new early personalized diagnosis ASD. We propose framework extract cerebral cortex from structural...

The management of athletic performance is immense importance in the sports industry. Performance concerned with maximizing athletes' and minimizing risk player injuries. Several factors are contributing to those objectives, including health status, emotional conditions (e.g., stress anxiety), load physical demands jumping landing tasks), etc. Generally, prediction injury a key component for prevention as successful identification predictors forms basis effective preventive measures. This...

A new technique for more accurate automatic segmentation of the kidney from its surrounding abdominal structures in diffusion-weighted magnetic resonance imaging (DW-MRI) is presented. This approach combines a 3D probabilistic shape model with first-order appearance and fourth-order spatial signal intensity to guide evolution geometric deformable model. The was built labeled training datasets produce spatially variant, independent random field region labels. Markov-Gibbs up interactions...

Task-based fMRI (TfMRI) is a diagnostic imaging modality for observing the effects of disease or other condition on functional activity brain. Autism spectrum disorder (ASD) pervasive developmental associated with impairments in social and linguistic abilities. Machine learning algorithms have been widely utilized brain aiming objective ASD diagnostics. Recently, deep methods gaining more attention classification. The goal this paper to develop convolutional neural network (CNN)-based...

In developed countries, age-related macular degeneration (AMD), a retinal disease, is the main cause of vision loss in elderly. Optical Coherence Tomography (OCT) currently gold standard for assessing individuals initial AMD diagnosis. this paper, we look at how OCT imaging can be used to diagnose AMD. Our aim examine and compare automated computer-aided diagnostic (CAD) systems diagnosing grading We provide brief summary, outlining aspects performance assessment providing basis current...

Prostate cancer is a significant health concern with high mortality rates and substantial economic impact. Early detection plays crucial role in improving patient outcomes. This study introduces non-invasive computer-aided diagnosis (CAD) system that leverages intravoxel incoherent motion (IVIM) parameters for the of prostate (PCa). IVIM imaging enables differentiation water molecule diffusion within capillaries outside vessels, offering valuable insights into tumor characteristics. The...

Background and Objective: The use of computer-aided detection (CAD) systems can help radiologists make objective decisions reduce the dependence on invasive techniques. In this study, a CAD system that detects identifies prostate cancer from diffusion-weighted imaging (DWI) is developed. Methods: proposed first uses non-negative matrix factorization (NMF) to integrate three different types features for accurate segmentation regions. Then, discriminatory in form apparent diffusion coefficient...

Early diagnosis and effective treatment of age-related macular degeneration (AMD), a leading cause vision impairment, are critically dependent on accurate grading. This paper presents novel framework, named Mask-UnMask Regions (MUMR), designed to differentiate between normal retina, intermediate AMD, geographic atrophy (GA), wet AMD using standardized retinal fundus images with an input resolution 1024 × pixels. The framework initiates the downscaling quarter their original size via...

<title>Abstract</title> Water treatment is a critical process for ensuring public health and maintaining environmental sustainability. This research investigates the application of ultrasonic technology as an innovative method to enhance water processes. Unlike conventional methods, ultrasound utilizes high- frequency waves generate cavitation, leading disruption contaminants microorganisms. The study focuses on optimizing parameters, including frequency, power density, irradiation time,...