Intelligent Medical Diagnostic System for Hepatitis B

The hepatitis B virus is the most deadly virus,which significantly affects the human liver.The termination of the hepatitis B virus is mandatory and can be done by taking precautions as well as a suitable cure in its introductory stage;otherwise,it will become a severe problem and make a human liver suffer from the most dangerous diseases,such as liver cancer.In this paper,two medical diagnostic systems are developed for the diagnosis of this life-threatening virus.The methodologies used to develop thesemodels are fuzzy logic and the neuro-fuzzy technique.The diverse parameters that assist in the evaluation of performance are also determined by using the observed values from the proposed system for both developedmodels.The classification accuracy of a multilayered fuzzy inference system is 94%.The accuracy with which the developed medical diagnostic system by using Adaptive Network based Fuzzy Interference System(ANFIS)classifies the result corresponding to the given input is 95.55%.The comparison of both developed models on the basis of their performance parameters has been made.It is observed that the neuro-fuzzy technique-based diagnostic system has better accuracy in classifying the infected and non-infected patients as compared to the fuzzy diagnostic system.Furthermore,the performance evaluation concluded that the outcome given by the developed medical diagnostic system by using ANFIS is accurate and correct as compared to the developed fuzzy inference system and also can be used in hospitals for the diagnosis of Hepatitis B disease.In other words,the adaptive neuro-fuzzy inference system has more capability to classify the provided inputs adequately than the fuzzy inference system.

Standard diagnostic report of medical imaging

1 BackgroundIt is well known that the radiology diagnostic report as the essential component of the patient′s permanent health record,which radiography is an indispensable diagnostic tool.Our duties are observe the imaging carefully and write a

Face animation based on multiple sources and perspective alignment

Background Face image animation generates a synthetic human face video that harmoniously integrates the identity derived from the source image and facial motion obtained from the driving video.This technology could be beneficial in multiple medical fields,such as diagnosis and privacy protection.Previous studies on face animation often relied on a single source image to generate an output video.With a significant pose difference between the source image and the driving frame,the quality of the generated video is likely to be suboptimal because the source image may not provide sufficient features for the warped feature map.Methods In this study,we propose a novel face-animation scheme based on multiple sources and perspective alignment to address these issues.We first introduce a multiple-source sampling and selection module to screen the optimal source image set from the provided driving video.We then propose an inter-frame interpolation and alignment module to further eliminate the misalignment between the selected source image and the driving frame.Conclusions The proposed method exhibits superior performance in terms of objective metrics and visual quality in large-angle animation scenes compared to other state-of-the-art face animation methods.It indicates the effectiveness of the proposed method in addressing the distortion issues in large-angle animation.

Fuzzy Logic-Based System for Liver Fibrosis Disease

The diagnosis of liver fibrosis(LF)is crucial as it is a deadly and life-threatening disease.Artificial intelligence techniques aid doctors by using the previous data on health and making a diagnostic system,which helps to take decisions about patients’health as experts can.The historical data of a patient’s health can have vagueness,inaccurate,and can also have missing values.The fuzzy logic theory can deal with these issues in the dataset.In this paper,a multilayer fuzzy expert system is developed to diagnose LF.The model is created by using multiple layers of the fuzzy logic approach.This system aids in classifying the health of patients into different classes.The proposed method has two layers,i.e.,layer 1 and layer 2.The input variables used in layer 1 for diagnosing liver fibrosis are Appetite,Jaundice,Ascites,Age,and Fatigue.Similarly,in layer 2,the input variables are Platelet count,White blood cell count,spleen,SGPT ALT(Serum Glutamic Pyruvic Transaminase Alanine Aminotransferase),SGOT ALT(Serum Glutamicoxalacetic Transaminase Alanine Aminotransferase),Serum bilirubin,and Serum albumin.The output variables for this developed system are no damage,minimal damage,significant damage,severe damage,and cirrhosis.This research work also presents the examination of results based on performance parameters.The proposed system achieves a classification accuracy of 95%.Moreover,other performance parameters such as sensitivity,specificity,and precision are calculated as 97.14%,92%,and 94.44%,respectively.

COLLAGEN AS IN VIVO QUANTITATIVE FLUORESCENT BIOMARKERS OF ABNORMAL TISSUE CHANGES

Collagen is an endogenousfluorophore that accounts for about 70%of all proteins of human skin,so it can be an optical marker for structural abnormalities in tissues registered by laserfluorescent diagnostics in vivo.Using the examples of such abnormalities as scars,scleroderma and basal cell carcinoma,this study shows the differences between coefficients offluorescent contrast k_(f)(λ)of abnormalities from the ones for healthy tissues atfluorescent excitation wavelength 360
380 nm.It is shown that scars and dysplasia are characterized by reduced values of k_(f)(λ)for collagen.Due to high turbidity and phase heterogeneousness as well as variation of parameters of blood microcirculation and concentrations of other related chromophores,there is no mathematical model that precisely calculates the concentration of collagen in tissues only with the use of the value offluorescent signal intensity.So,probably,the best marker of the pathological process is a comprehensive representation of k_(f)(λ)for all endogenousfluorophores,i.e.,for all used visible wavelengths.In this case identification of abnormal tissues is quite possible by detecting some deviations of coefficients k_(f)(λ)for the optically identical and symmetrical regions of the human body.

A Novel Convolutional Neural Network Model for Malaria Cell Images Classification

Infectious diseases are an imminent danger that faces human beings around the world.Malaria is considered a highly contagious disease.The diagnosis of various diseases,including malaria,was performed manually,but it required a lot of time and had some human errors.Therefore,there is a need to investigate an efficient and fast automatic diagnosis system.Deploying deep learning algorithms can provide a solution in which they can learn complex image patterns and have a rapid improvement in medical image analysis.This study proposed a Convolutional Neural Network(CNN)model to detect malaria automatically.A Malaria Convolutional Neural Network(MCNN)model is proposed in this work to classify the infected cases.MCNN focuses on detecting infected cells,which aids in the computation of parasitemia,or infection measures.The proposed model achieved 0.9929,0.9848,0.9859,0.9924,0.0152,0.0141,0.0071,0.9890,0.9894,and 0.9780 in terms of specificity,sensitivity,precision,accuracy,F1-score,and Matthews Correlation Coefficient,respectively.A comparison was carried out between the proposed model and some recent works in the literature.This comparison demonstrates that the proposed model outperforms the compared works in terms of evaluation metrics.

Unraveling the nuclear isotope tapestry:Applications,challenges,and future horizons in a dynamic landscape

Nuclear isotopes,distinct atoms characterized by varying neutron counts,have profoundly influenced a myriad of sectors,spanning from medical diagnostics and therapeutic interventions to energy production and defense strategies.Their multifaceted applications have been celebrated for catalyzing revolutionary breakthroughs,yet these advancements simultaneously introduce intricate challenges that warrant thorough investigation.These challenges encompass safety protocols,potential environmental detriments,and the complex geopolitical landscape surrounding nuclear proliferation and disarmament.This comprehensive review embarks on a deep exploration of nuclear isotopes,elucidating their nuanced classifications,wide-ranging applications,intricate governing policies,and the multifaceted impacts of their unintended emissions or leaks.Furthermore,the study meticulously examines the cutting-edge remediation techniques currently employed to counteract nuclear contamination while projecting future innovations in this domain.By weaving together historical context,current applications,and forward-looking perspectives,this review offers a panoramic view of the nuclear isotope landscape.In conclusion,the significance of nuclear isotopes cannot be understated.As we stand at the crossroads of technological advancement and ethical responsibility,this review underscores the paramount importance of harnessing nuclear isotopes'potential in a manner that prioritizes safety,sustainability,and the greater good of humanity.

Programmable microfluidic manipulations for biomedical applications

Fluid manipulation plays an important role in biomedical applications such as biochemical assays,medical diag-nostics,and drug development.Programmable fluidic manipulation at the microscale is highly desired in both fundamental and practical aspects.In this paper,we summarize some of the latest studies that achieve pro-grammable fluidic manipulation through intricate capillaric circuits design,construction of biomimetic metasur-face,and responsive surface wettability control.We highlight the working principle of each system and concisely discuss their design criterion,technical improvements,and implications for future study.We envision that with multidisciplinary efforts,microfluidics would continue to bring vast opportunities to biomedical fields and make contributions to human health.