FedPneu: Federated Learning for Pneumonia Detection across Multiclient Cross-Silo Healthcare Datasets.

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 of an advanced deep-learning model by using a federated learning framework. The deep learning models rely on the utilization of a centralized system for disease prediction on the medical imaging data and pose risks of data breaches and exploitation; however, federated learning is a decentralized architecture which significantly reduces data privacy concerns.

Segmentation Synergy with a Dual U-Net and Federated Learning with CNNRF Models for Enhanced Brain Tumor Analysis.

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 help in terms of brain tumor identification accuracy from MRI data. Objective This research paper aims to check the efficiency of a federated learning method that joins two classifiers, such as convolutional neural networks (CNNs) and random forests (R.

Detailed-based dictionary learning for low-light image enhancement using camera response model for industrial applications.

Images captured in low-light environments are severely degraded due to insufficient light, which causes the performance decline of both commercial and consumer devices. One of the major challenges lies in how to balance the image enhancement properties of light intensity, detail presentation, and colour integrity in low-light enhancement tasks. This study presents a novel image enhancement framework using a detailed-based dictionary learning and camera response model (CRM).

Enhanced low-light image fusion through multi-stage processing with Bayesian analysis and quadratic contrast function.

This manuscript introduces an innovative multi-stage image fusion framework that adeptly integrates infrared (IR) and visible (VIS) spectrum images to surmount the difficulties posed by low-light settings. The approach commences with an initial preprocessing stage, utilizing an Efficient Guided Image Filter for the infrared (IR) images to amplify edge boundaries and a function for the visible (VIS) images to boost local contrast and brightness. Utilizing a two-scale decomposition technique that incorporates Lipschitz constraints-based smoothing, the images are effectively divided into distinct base and detail layers, thereby guaranteeing the preservation of essential structural information.

Effective image fusion strategies in scientific signal processing disciplines: Application to cancer and carcinoma treatment planning.

Multimodal medical image fusion is a perennially prominent research topic that can obtain informative medical images and aid radiologists in diagnosing and treating disease more effectively. However, the recent state-of-the-art methods extract and fuse features by subjectively defining constraints, which easily distort the exclusive information of source images. To overcome these problems and get a better fusion method, this study proposes a 2D data fusion method that uses salient structure extraction (SSE) and a swift algorithm via normalized convolution to fuse different types of medical images.

Machine Learning-based Deep Analysis of Human Blood using NIR Spectrophotometry Signatures.

Background: Non-invasive bio-diagnostics are essential for providing patients with safer treatment. In this subject, significant growth is attained for noninvasive anaemia detection in terms of Hb concentration by means of spectroscopic and image analysis. The lower satisfaction rate is found due to inconsistent results in various patient settings.

Multimodal Medical Image Fusion Utilizing Two-scale Image Decomposition via Saliency Detection.

Background: Modern medical imaging modalities used by clinicians have many applications in the diagnosis of complicated diseases. These imaging technologies reveal the internal anatomy and physiology of the body. The fundamental idea behind medical image fusion is to increase the image's global and local contrast, enhance the visual impact, and change its format so that it is better suited for computer processing or human viewing while preventing noise magnification and accomplishing excellent real-time performance.

Fusion of Multimodal Medical Images Based on Fine-Grained Saliency and Anisotropic Diffusion Filter.

Background: A clinical medical image provides vital information about a person's health and bodily condition. Typically, doctors monitor and examine several types of medical images individually to gather supplementary information for illness diagnosis and treatment. As it is arduous to analyze and diagnose from a single image, multi-modality images have been shown to enhance the precision of diagnosis and evaluation of medical conditions.

The Evolution of Medical Imaging in the Therapeutics of Patients with Skin Cancer.

Introduction: Medical imaging mechanization has reformed medical management, empowering doctors to recognize cancer prematurely and promote patient outcomes. Imaging tests are of significant influence in the detection and supervision of cancer patients. Cancer recognition generally necessitates imaging studies that, in most instances, utilize a trivial amount of radiation.

A constructive non-local means algorithm for low-dose computed tomography denoising with morphological residual processing.

Low-dose computed tomography (LDCT) has attracted significant attention in the domain of medical imaging due to the inherent risks of normal-dose computed tomography (NDCT) based X-ray radiations to patients. However, reducing radiation dose in CT imaging produces noise and artifacts that degrade image quality and subsequently hinders medical disease diagnostic performance. In order to address these problems, this research article presents a competent low-dose computed tomography image denoising algorithm based on a constructive non-local means algorithm with morphological residual processing to achieve the task of removing noise from the LDCT images.

Progressive evaluation in spectroscopic sensors for non-invasive blood haemoglobin analysis-a review.

Frequent monitoring of haemoglobin concentration is highly recommended by physicians to diagnose anaemia and polycythemia vera. Moreover, other conditions that also demand assessment of haemoglobin are blood loss, before blood donation, during pregnancy, and preoperative, perioperative and postoperative conditions. The cyanmethemoglobin/haemiglobincyanide method, portable haemoglobinometers and haematology analyzers are some of the standard methods used to diagnose the aforementioned ailments.

NIR-based Sensing System for Non-invasive Detection of Hemoglobin for Point-of-care Applications.

Background: Hemoglobin is an essential biomolecule for the transportation of oxygen, therefore, its assessment is also important to be done frequently in numerous clinical practices. Traditional invasive techniques have concomitant shortcomings, such as time delay, the onset of infections, and discomfort, which necessitate a non-invasive hemoglobin estimation solution to get rid of these constraints in health informatics. Currently, various techniques are underway in the allied domain, and scanty products are also feasible in the market.

Image Integration Procedures in Multisensory Medical Images: A Comprehensive Survey of the State-of-the-art Paradigms.

Background: Obtaining the medical history from a patient is a tedious task for doctors as it depends on a lot of factors which are difficult to keep track from a patient's perspective. Doctors have to rely upon technological tools to make a swift and accurate judgment about the patient's health.

Introduction: Out of many such tools, there are two special imaging modalities known as X-ray - Computed Tomography (CT) and Magnetic Resonance imaging (MRI) which are of significant importance in the medical world assisting the diagnosis process.