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.F.F.), with dual U-Net segmentation for federated learning. This procedure benefits the image identification task on preprocessed MRI scan pictures that have already been categorized. METHODS: In addition to using a variety of datasets, federated learning was utilized to train the CNN-RF model while taking data privacy into account. The processed MRI images with Median, Gaussian, and Wiener filters are used to filter out the noise level and make the feature extraction process easy and efficient. The surgical part used a dual U-Net layout, and the performance assessment was based on precision, recall, F1-score, and accuracy. RESULTS: The model achieved excellent classification performance on local datasets as CRPs were high, from 91.28% to 95.52% for macro, micro, and weighted averages. Throughout the process of federated averaging, the collective model outperformed by reaching 97% accuracy compared to those of 99%, which were subjected to different clients. The correctness of how data is used helps the federated averaging method convert individual model insights into a consistent global model while keeping all personal data private. CONCLUSION: The combined structure of the federated learning framework, CNN-RF hybrid model, and dual U-Net segmentation is a robust and privacypreserving approach for identifying MRI images from brain tumors. The results of the present study exhibited that the technique is promising in improving the quality of brain tumor categorization and provides a pathway for practical utilization in clinical settings.

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. The process of fusion is carried out in two distinct stages: firstly, a method grounded in Bayesian theory is employed to effectively combine the base layers, so effectively addressing any inherent uncertainty. Secondly, a Surface from Shade (SfS) method is utilized to ensure the preservation of the scene's geometry by enforcing integrability on the detail layers. Ultimately a Choose Max principle is employed to determine the most prominent textural characteristics, resulting in the amalgamation of the base and detail layers to generate an image that exhibits a substantial enhancement in both clarity and detail. The efficacy of our strategy is substantiated by rigorous testing, showcasing notable progressions in edge preservation, detail enhancement, and noise reduction. Consequently, our method presents significant advantages for real-world applications in image analysis.

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). It combines dictionary learning with edge-aware filter-based detail enhancement. It assumes each small detail patch could be sparsely characterised in the over-complete detail dictionary that was learned from many training detail patches using iterative ℓ 1 -norm minimization. Dictionary learning will effectively address several enhancement concerns in the progression of detail enhancement if we remove the visibility limit of training detail patches in the enhanced detail patches. We apply illumination estimation schemes to the selected CRM and the subsequent exposure ratio maps, which recover a novel enhanced detail layer and generate a high-quality output with detailed visibility when there is a training set of higher-quality images. We estimate the exposure ratio of each pixel using illumination estimation techniques. The selected camera response model adjusts each pixel to the desired exposure based on the computed exposure ratio map. Extensive experimental analysis shows an advantage of the proposed method that it can obtain enhanced results with acceptable distortions. The proposed research article can be generalised to address numerous other similar problems, such as image enhancement for remote sensing or underwater applications, medical imaging, and foggy or dusty conditions.

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. First, salient structure extraction (SSE) is used to attenuate the effect of noise and irrelevant data in the source images by preserving the significant structures. The salient structure extraction is performed to ensure that the pixels with a higher gradient magnitude impact the choices of their neighbors and further provide a way to restore the sharply altered pixels to their neighbors. In addition, a Swift algorithm is used to overcome the excessive pixel values and modify the contrast of the source images. Furthermore, the method proposes an efficient method for performing edge-preserving filtering using normalized convolution. In the end,the fused image are obtained through linear combination of the processed image and the input images based on the properties of the filters. A quantitative function composed of structural loss and region mutual data loss is designed to produce restrictions for preserving data at feature level and the structural level. Extensive experiments on CT-MRI images demonstrate that the proposed algorithm exhibits superior performance when compared to some of the state-of-the-art methods in terms of providing detailed information, edge contour, and overall contrasts.

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. OBJECTIVE: The top goal is to combine data from various modal images (CT/MRI and MR-T1/MR-T2) into a solitary image that, to the greatest degree possible, retains the key characteristics (prominent features) of the source images. METHODS: The clinical accuracy of medical issues is compromised because innumerable classical fusion methods struggle to conserve all the prominent features of the original images. Furthermore, complex implementation, high computation time, and more memory requirements are key problems of transform domain methods. With the purpose of solving these problems, this research suggests a fusion framework for multimodal medical images that makes use of a multi-scale edge-preserving filter and visual saliency detection. The source images are decomposed using a two-scale edge-preserving filter into base and detail layers. Base layers are combined using the addition fusion rule, while detail layers are fused using weight maps constructed using the maximum symmetric surround saliency detection algorithm. RESULTS: The resultant image constructed by the presumed method has improved objective evaluation metrics than other classical methods, as well as unhindered edge contour, more global contrast, and no ringing effect or artifacts. CONCLUSION: The methodology offers a dominant and symbiotic arsenal of clinical symptomatic, therapeutic, and biomedical research competencies that have the prospective to considerably strengthen medical practice and biological understanding.

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. OBJECTIVE: This observational study aims to present an adaptable point-of-care Near-Infrared (NIR) spectrophotometric approach with a constructive Machine Learning (ML) algorithm for monitoring Haemoglobin (Hb) concentration by considering dominating influencing factors into account. METHODS: To accomplish this objective, 121 subjects (19.2-55.4 years) were enrolled in the study, having a wide range of Hb concentrations (8.2-17.4 g/dL) obtained from two standard Laboratory analyzers. To inspect the performance, the unique dimensionality reduction approaches are applied with numerous regression models using 5-fold cross-validation. RESULTS: The optimum accuracy is found using support vector regression (SVR) and mutual information having 3 independent features i.e. Pearson correlation (r)= 0.79, standard deviation (SD)= 1.07 g/dL, bias=-0.13 g/dL and limits of agreement (LoA)=-2.22 to 1.97 g/dL. Additionally, comparability between two standard laboratory analyzers is found as; r=0.97, SD=0.50 g/dL, bias=0.21 g/dL, and LoA= -0.77 to 1.19 g/dL. CONCLUSION: The precision of ±1 g/dL in 5-fold cross-validation ensures the same performance irrespective of different age groups, gender, BMI, smoking level, drinking level, and skin type. The outcomes with the offered NIR sensing system and an exclusive ML algorithm can accelerate its' requirement at remote locative rural areas and critical care units where continuous Hb monitoring is compulsory.

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. OBJECTIVE: Several conventional image fusion techniques strengthen the consistency of the information by combining varied image observations; nevertheless, the drawback of these techniques in retaining all crucial elements of the original images can have a negative impact on the accuracy of clinical diagnoses. This research develops an improved image fusion technique based on fine-grained saliency and an anisotropic diffusion filter to preserve structural and detailed information of the individual image. METHOD: In contrast to prior efforts, the saliency method is not executed using a pyramidal decomposition, but rather an integral image on the original scale is used to obtain features of superior quality. Furthermore, an anisotropic diffusion filter is utilized for the decomposition of the original source images into a base layer and a detail layer. The proposed algorithm's performance is then contrasted to those of cutting-edge image fusion algorithms. RESULTS: The proposed approach cannot only cope with the fusion of medical images well, both subjectively and objectively, according to the results obtained, but also has high computational efficiency. CONCLUSION: Furthermore, it provides a roadmap for the direction of future research.

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. Methodologies such as X-rays, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) are predominant in clinical managerial, incorporating remedy and research. BACKGROUND: Over recent years, diagnostic imaging has progressed from a state of commencement to an advanced level. Numerous modern imaging procedures have evolved. Although contemporary medical imaging comprises image exhibition together with image refining, computer-aided diagnosis (CAD), image inscribing and conserving, and image transference, the majority of which are embraced in picture documentation and communication processes. AIM: This review targets to encapsulate toxicology information on skin cancer unpredictability essential to interpretation measures, report important factor that helps in defining skin cancer condition, and possible medical care alternatives or medical attention endorsed referring to diverse aspects involving the size and site of malignancy, the complications, patient's priority and well being. We concisely review various therapy alternatives, methods of radiation autoimmunity, prime observational study designs of medical and distinct radiation resources and cancer risks, and current analysis methodologies and research precision. CONCLUSION: The detail of this paper covers a brief review of research and evolution in medical imaging discipline and mechanism. This review considers the physiology of melanocytes and the pathogenesis of skin cancer using medical imaging. Also, a description of risk factors, prevention methods, screening, various diagnosis methods and different stages of skin cancer, sub-types and different types of treatment methods is provided in this paper for research and development.

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. We propose an innovative constructive non-local image filtering algorithm by means of applications in low-dose computed tomography technology. The nonlocal mean filter that was recently proposed was modified to construct our denoising algorithm. It constructs the discrete property of neighboring filtering to enable rapid vectorized and parallel implantation in contemporary shared memory computer platforms while simultaneously decreases computing complexity. Subsequently, the proposed method performs faster computation compared to a non-vectorized and serial implementation in terms of speed and scales linearly with image dimension. In addition, the morphological residual processing is employed for the purpose of edge-preserving image processing. It combines linear lowpass filtering with a nonlinear technique that enables the extraction of meaningful regions where edges could be preserved while removing residual artifacts from the images. Experimental results demonstrate that the proposed algorithm preserves more textural and structural features while reducing noise, enhances edges and significantly improves image quality more effectively. The proposed research article obtains better results both qualitatively and quantitively when compared to other comparative algorithms on publicly accessible datasets.

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. METHODS: The advancement in signal processing theory and analysis has led to the design and implementation of a large number of image processing and fusion algorithms. Each of these methods has evolved in the terms of their computational efficiency and visual results over the years. RESULTS: Various researches have revealed their properties in terms of their efficiency and outreach and it has been concluded that image fusion can be a very suitable process that can help to compensate for the drawbacks. CONCLUSION: In this manuscript, recent state-of-the-art techniques have been used to fuse these image modalities and established its need and importance in a more intuitive way with the help of a wide range of assessment parameters.

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. However, due to the low satisfaction rate, invasive solutions are still assumed as the gold standard. Recently introduced technologies effectively evolved as optical spectroscopy and digital photographic concepts on different sensing spots, e.g., fingertip, palpebral conjunctiva, bulbar conjunctiva, and fingernail. Productive sensors develop more than eight wavelengths to compute hemoglobin concentration and four wavelengths to display only Hb-index (trending of hemoglobin) either in disposable adhesive or reusable cliptype sensor's configuration. OBJECTIVE: This study aims at an optimistic optical spectroscopic technique to measure hemoglobin concentration and conditional usability of non-invasive blood parameters' diagnostics at point-ofcare. METHODS: Two distinguishable light emitting sources (810 nm and 1300 nm) are utilized at isosbestic points with a single photodetector (800-1700 nm). With this purpose, reusable finger probe assembly is facilitated in transmittance mode based on the newly offered sliding mechanism to block ambient light. RESULTS: Investigation with proposed design presents correlation coefficients between reference hemoglobin and every individual feature, a multivariate linear regression model for highly correlated independent features. Moreover, principal component analytical model with multivariate linear regression offers mean bias of 0.036 and -0.316 g/dL, precision of 0.878 and 0.838 and limits of agreement from -1.685 to 1.758 g/dL and -1.790 to 1.474 g/dL for 18 and 21 principal components, respectively. CONCLUSION: The encouraging readouts emphasize favorable precision; therefore, it is proposed that the sensing system is amenable to assess hemoglobin in settings with limited resources and strengthening future routes for the point of care applications.

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

Objective.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. However, discomfort, delay and risk of infection are typical limitations of traditional measuring solutions. These limitations create the necessity to develop a non-invasive haemoglobin monitoring technique for a better lifestyle.Approach.Various methods and products have already been developed and are popular due to their non-invasiveness; however, invasive solutions are still considered as the reference standard method. Therefore, this review summarizes the attributes of existing non-invasive solutions. These attributes are finalized as brief details, accuracy, optimal benefits and research challenges for exploring potential gaps, advancements and possibilities to consider as futuristic alternative methodologies.Main results.Non-invasive total haemoglobin assessment techniques are mainly based on optical spectroscopy (reflectance/transmittance) or digital photography, or spectroscopic imaging in spot-check/continuous monitoring mode. In all these techniques, we have noticed that there is a need to consider different light conditions, motion artefacts, melanocytes, other blood constituents, smoking and precise fixing of the sensor from the sensing spot for exact formulation.Significance.Moreover, based on careful and critical analysis of outcomes, none of these techniques or products are used independently or intended to replace invasive laboratory testing. Therefore, there is a requirement for a more accurate technique that can eliminate the requirement for blood samples and likely end up as a reference standard method.