Ayush Dogra
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Underwater image enhancement using colour balancing and morphological residual processing through gamma correction Open
Underwater images typically suffer from poor visibility, low contrast, and severe color distortion caused by wavelength-dependent absorption and scattering of light. These degradations not only reduce visual quality but also affect subsequ…
Patch-Wise Local Principal Component Analysis-based Medical Image Denoising: A Method Noise Approach Open
Introduction Gaussian noise is often added during the acquisition or transmission of medical images, which can blur important organs and reduce diagnostic accuracy. To address this issue, a hybrid denoising model that combines BayesShrink …
Multimodal Medical Image Fusion: Techniques, Databases, Evaluation Metrics, and Clinical Applications -A Comprehensive Review Open
Multi-modal Medical Image Fusion (MMIF) is an advancing field at the intersection of medical imaging, data science, and clinical diagnostics. It aims to integrate complementary data from various imaging modalities, such as MRI, CT, and PET…
View article: Structure-aware medical image fusion via mean curvature enhancement in the contourlet domain
Structure-aware medical image fusion via mean curvature enhancement in the contourlet domain Open
The medical image fusion is a critical application in medical diagnosis, where anatomical and functional information from different imaging modalities, e.g., Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) can be integrated. …
A Review and Experimental Analysis of Denoising Techniques for Medical Images Open
Introduction Magnetic Resonance Imaging (MRI) and High-Resolution Computed Tomography (HRCT) are crucial for comprehensive diagnosis and treatment planning, as they provide detailed anatomical information. However, noise introduced during …
PHASEY: A Contrastive Learning Approach for Enhanced Human Gait Phases Recognition Open
Human gait has gained much attention in behavioral biometrics as it possesses unique and distinctive characteristics. Gait phases, which describe the different patterns of human walking, are significant for the analysis and understanding o…
View article: Edge-aware multisensor brain image fusion via guided filtering in Laplacian domain
Edge-aware multisensor brain image fusion via guided filtering in Laplacian domain Open
Medical image fusion is crucial in clinical diagnosis since it enhances diagnostic accuracy by integrating complementary data from many modalities, including Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and Positron Emission …
View article: Synergistic Integration of 3D CNN and Vision Transformers for Enhanced Bio-Medical for Knee Cartilage Pathology Detection
Synergistic Integration of 3D CNN and Vision Transformers for Enhanced Bio-Medical for Knee Cartilage Pathology Detection Open
Degeneration of knee cartilage is a significant health concern, particularly among the elderly and individuals with a history of joint pain. Early diagnosis and classification are crucial for effective intervention and treatment. The propo…
Non-uniform Compression of Magnetic Resonance Brain Images Using Edge-based Active Contours Driven by Maximum Entropy Threshold Open
Introduction With the exponential growth of digital imaging data, the compression of medical images has become a critical issue for efficient storage and reliable transmission. Researchers are continuously exploring new methods for reducin…
Comparative Analysis on Prediction of Chronic KidneyDisease by various Machine Learning Models Open
Chronic Kidney Disease (CKD) is a prevalent global healthconcern, which is affecting a number of people in the whole world. Earlydetection & precise checking of CKD are really important for essentialsupervision & intervention to prevent di…
Fetal Diagnostics using Vision Transformer for Enhanced Health and Severity Prediction in Ultrasound Imaging Open
Aim: This research aims to develop and evaluate a novel health classification and severity detection system based on Vision Transformers (ViTs) for fetal ultrasound imagery. This contributes to improved precision in fetal health status det…
Segmented MR Images by RG-FCM subjected to Non-Uniform Compression comprising Cascade of different Encoders Open
Introduction: The fundamental problem with the transmission and storage of medical images is their inherent redundancy and large size necessitating higher bandwidth and a significant amount of storage space. Objectives: The main objective …
Information Modeling Technique to Decipher Research Trends of Federated Learning in Healthcare Open
Aim The aim of this study is to determine the most prevalent types of federated learning, discuss their uses in healthcare, highlight the most significant issues, and suggest methods for further research. Context When it comes to handling …
View article: A privacy-preserved horizontal federated learning for malignant glioma tumour detection using distributed data-silos
A privacy-preserved horizontal federated learning for malignant glioma tumour detection using distributed data-silos Open
Malignant glioma is the uncontrollable growth of cells in the spinal cord and brain that look similar to the normal glial cells. The most essential part of the nervous system is glial cells, which support the brain’s functioning prominentl…
Transforming Retinal Diagnostics: Advanced Detection of Diabetic Retinopathy Using Vision Transformers and Capsule Networks Open
Diabetic Retinopathy (DR), nowadays is one of the leading causes of blindness worldwide, it is a severe complication of diabetes mellitus that affects the retina blood vessels. Accurate diagnosis depends on early detection of DR. The study…
Hybrid ViT-CapsNet Framework for Brain Tumor Diagnosis Using Biomedical MRI Open
Brain tumor identification through Bio-medical magnetic resonance imaging (MRI) presents a critical challenge in diagnostic imaging, where high accuracy is essential for informed treatment planning. Traditional methods face limitations in …
View article: FedPneu: Federated Learning for Pneumonia Detection across Multiclient Cross-Silo Healthcare Datasets
FedPneu: Federated Learning for Pneumonia Detection across Multiclient Cross-Silo Healthcare Datasets Open
Background: Pneumonia is an acute respiratory infection that has emerged as the predominant catalyst for escalating mortality rates worldwide. In the pursuit of the prevention and prediction of pneumonia, this work employs the development …
Dehazing Mechanism Using Auto-Encoder with Intensity Attention System Open
In the modern world, images play a significant medium for communication. Primarily, it is easily transferred and disseminated across various platforms which allows the people to express their ideology and perceptions. Conversely, images ca…
New Perspective of Multi-dimensional Approach for the Management of Attention-deficit Hyperactivity Disorder: A Review Open
One of the most common mental diseases in childhood, attention-deficit/hyperactivity disorder (ADHD) often lasts into adulthood for many individuals. The neurodevelopmental condition known as ADHD impacts three areas of the brain: hyperact…
Chronic Kidney Disease Detection Using Machine Learning: From Analysis to Framework Development Open
Considering the aspects of sustainable development goals, Good health and well-being ensure the development of a nation. Chronic kidney disease (CKD) is a progressive and irreversible condition characterized by the gradual loss of kidney f…
Attention Deficit Hyperactivity Disorder Symptoms Among Students Attending Higher Educational Institution: A Cross-Sectional Study Open
Background A complicated and clinically varied illness known as ADHD (“Attention-deficit/hyperactivity disorder”) leads to poor academic and professional outcomes, family stress, and financial difficulty. Worldwide, children and adults wit…
Segmentation Synergy with a Dual U-Net and Federated Learning with CNNRF Models for Enhanced Brain Tumor Analysis Open
Background: Brain tumours represent a diagnostic challenge, especially in the imaging area, where the differentiation of normal and pathologic tissues should be precise. The use of up-to-date machine learning techniques would be of great h…
Revolutionizing Historical Manuscript Analysis: A Deep Learning Approach with Intelligent Feature Extraction for Script Classification Open
The automated classification of historical document scripts holds profound implications for historians, providing unprecedented insights into the contexts of ancient manuscripts. This study introduces a robust deep learning system integrat…