Spatial Correlation Module for Classification of Multi-Label Ocular Diseases Using Color Fundus Images

To prevent irreversible damage to one’s eyesight,ocular diseases(ODs)need to be recognized and treated immediately.Color fundus imaging(CFI)is a screening technology that is both effective and economical.According to CFIs,the early stages of the disease are characterized by a paucity of observable symptoms,which necessitates the prompt creation of automated and robust diagnostic algorithms.The traditional research focuses on image-level diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of eyes.In addition,they usually only target one or a few different kinds of eye diseases at the same time.In this study,we design a patient-level multi-label OD(PLML_ODs)classification model that is based on a spatial correlation network(SCNet).This model takes into consideration the relevance of patient-level diagnosis combining bilateral eyes and multi-label ODs classification.PLML_ODs is made up of three parts:a backbone convolutional neural network(CNN)for feature extraction i.e.,DenseNet-169,a SCNet for feature correlation,and a classifier for the development of classification scores.The DenseNet-169 is responsible for retrieving two separate sets of attributes,one from each of the left and right CFI.After then,the SCNet will record the correlations between the two feature sets on a pixel-by-pixel basis.After the attributes have been analyzed,they are integrated to provide a representation at the patient level.Throughout the whole process of ODs categorization,the patient-level representation will be used.The efficacy of the PLML_ODs is examined using a soft margin loss on a dataset that is readily accessible to the public,and the results reveal that the classification performance is significantly improved when compared to several baseline approaches.

Mechanisms and molecular approaches for heat tolerance in rice (Oryza sativa L.) under climate change scenario

Rice, a staple cereal crop in many parts of the world, has been confronted with multiple environmental stresses including high temperature, negatively impacts the booting as well as anthesis growth stages. The situation is further complicated by the changing climatic conditions, resulting in gradual escalation of temperature as well as changing the rainfall pattern and frequency, thus raising a concern of food security worldwide. The situation can be combat by developing rice varieties with excellent genetics with improved morpho-physiological, biochemical, and molecular mechanisms, together can minimize the adverse effects of heat stress. Here, several strategies(encompassing genetic and genomic, and mechanisms involved) for mitigating the impact of high temperature on rice have been discussed. Finally, the utilization of genomic knowledge in augmenting the conventional breeding approaches have been comprehensively elaborated to develop heat tolerant germplasm.

Deep Learning Based Classification of Wrist Cracks from X-ray Imaging

Wrist cracks are the most common sort of cracks with an excessive occurrence rate.For the routine detection of wrist cracks,conventional radiography(X-ray medical imaging)is used but periodically issues are presented by crack depiction.Wrist cracks often appear in the human arbitrary bone due to accidental injuries such as slipping.Indeed,many hospitals lack experienced clinicians to diagnose wrist cracks.Therefore,an automated system is required to reduce the burden on clinicians and identify cracks.In this study,we have designed a novel residual network-based convolutional neural network(CNN)for the crack detection of the wrist.For the classification of wrist cracks medical imaging,the diagnostics accuracy of the RN-21CNN model is compared with four well-known transfer learning(TL)models such as Inception V3,Vgg16,ResNet-50,and Vgg19,to assist the medical imaging technologist in identifying the cracks that occur due to wrist fractures.The RN-21CNN model achieved an accuracy of 0.97 which is much better than its competitor`s approaches.The results reveal that implementing a correct generalization that a computer-aided recognition system precisely designed for the assistance of clinician would limit the number of incorrect diagnoses and also saves a lot of time.

Hep-Pred: Hepatitis C Staging Prediction Using Fine Gaussian SVM

Hepatitis C is a contagious blood-borne infection,and it is mostly asymptomatic during the initial stages.Therefore,it is difficult to diagnose and treat patients in the early stages of infection.The disease’s progression to its last stages makes diagnosis and treatment more difficult.In this study,an AI system based on machine learning algorithms is presented to help healthcare professionals with an early diagnosis of hepatitis C.The dataset used for our Hep-Pred model is based on a literature study,and includes the records of 1385 patients infected with the hepatitis C virus.Patients in this dataset received treatment dosages for the hepatitis C virus for about 18 months.A former study divided the disease into four main stages.These stages have proven helpful for doctors to analyze the liver’s condition.The traditional way to check the staging is the biopsy,which is a painful and time-consuming process.This article aims to provide an effective and efficient approach to predict hepatitis C staging.For this purpose,the proposed technique uses a fine Gaussian SVM learning algorithm,providing 97.9%accurate results.

Fusion-Based Machine Learning Architecture for Heart Disease Prediction

The contemporary evolution in healthcare technologies plays a considerable and signicant role to improve medical services and save human lives.Heart disease or cardiovascular disease is the most fatal and complex disease which it is hardly to be detected through our naked eyes,as numerous people have been suffering from this disease globally.Heart attacks occur when the ranges of vital signs such as blood pressure,pulse rate,and body temperature exceed their normal values.The efcient diagnosis of heart diseases could play a substantial role in the eld of cardiology,while diagnostic time could be reduced.It has been a key challenge for researchers and medical experts to diagnose heart diseases accurately and timely.Therefore,machine learning-based techniques are used for the diagnosis with higher accuracy,using datasets compiled from former medical patients’reports.In recent years,numerous studies have been presented in the literature propose machine learning techniques for diagnosing heart diseases.However,the existing techniques have some limitations in terms of their accuracy.In this paper,a novel Support Vector Machine(SVM)based architecture for heart disease prediction,empowered with a fuzzy based decision level fusion,is presented.The SVMbased architecture has improved the accuracy signicantly as compared to existing solutions,where 96.23%accuracy has been achieved.

Osteoporosis Prediction for Trabecular Bone using Machine Learning: A Review

Trabecular bone holds the utmost importance due to its significance regarding early bone loss.Diseases like osteoporosis greatly affect the structure of the Trabecular bone which results in different outcomes like high risk of fracture.The objective of this paper is to inspect the characteristics of the Trabecular Bone by using the Magnetic Resonance Imaging(MRI)technique.These characteristics prove to be quite helpful in studying different studies related to Trabecular bone such as osteoporosis.The things that were considered before the selection of the articles for the systematic review were language,research field,and electronic sources.Only those articles written in the English language were selected as it is the most prominent language used in scientific,engineering,computer science,and biomedical researches.This literature review was conducted on the articles published between 2006 and 2020.A total of 62 research papers out of 1050 papers were extracted which were according to our topic of review after screening abstract and article content for the title and abstract screening.The findings from those researches were compiled at the end of the result section.This systematic literature review presents a comprehensive report on scientific researches and studies that have been done in the medical area concerning trabecular bone.

A Weighted Spatially Constrained Finite Mixture Model for Image Segmentation

Spatially Constrained Mixture Model(SCMM)is an image segmentation model that works over the framework of maximum a-posteriori and Markov Random Field(MAP-MRF).It developed its own maximization step to be used within this framework.This research has proposed an improvement in the SCMM’s maximization step for segmenting simulated brain Magnetic Resonance Images(MRIs).The improved model is named as the Weighted Spatially Constrained Finite Mixture Model(WSCFMM).To compare the performance of SCMM and WSCFMM,simulated T1-Weighted normal MRIs were segmented.A region of interest(ROI)was extracted from segmented images.The similarity level between the extracted ROI and the ground truth(GT)was found by using the Jaccard and Dice similarity measuring method.According to the Jaccard similarity measuring method,WSCFMM showed an overall improvement of 4.72%,whereas the Dice similarity measuring method provided an overall improvement of 2.65%against the SCMM.Besides,WSCFMM signicantly stabilized and reduced the execution time by showing an improvement of 83.71%.The study concludes that WSCFMM is a stable model and performs better as compared to the SCMM in noisy and noise-free environments.

Fungal diversity notes 1611-1716: taxonomic and phylogenetic contributions on fungal genera and species emphasis in south China

This article is the 15th contribution in the Fungal Diversity Notes series,wherein 115 taxa from three phyla,nine classes,28 orders,48 families,and 64 genera are treated.Fungal taxa described and illustrated in the present study include a new family,five new genera,61 new species,five new combinations,one synonym,one new variety and 31 records on new hosts or new geographical distributions.Ageratinicolaceae fam.nov.is introduced and accommodated in Pleosporales.The new genera introduced in this study are Ageratinicola,Kevinia,Pseudomultiseptospora(Parabambusicolaceae),Marasmiellomycena,and Vizzinia(Porotheleaceae).Newly described species are Abrothallus altoandinus,Ageratinicola kunmingensis,Allocryptovalsa aceris,Allophoma yuccae,Apiospora cannae,A.elliptica,A.pallidesporae,Boeremia wisteriae,Calycina papaeana,Clypeo-coccum lichenostigmoides,Coniochaeta riskali-shoyakubovii,Cryphonectria kunmingensis,Diaporthe angustiapiculata,D.campylandrae,D.longipapillata,Diatrypella guangdongense,Dothiorella franceschinii,Endocalyx phoenicis,Epicoc-cum terminosporum,Fulvifomes karaiensis,F.pannaensis,Ganoderma ghatensis,Hysterobrevium baoshanense,Inocybe avellaneorosea,I.lucida,Jahnula oblonga,Kevinia lignicola,Kirschsteiniothelia guangdongensis,Laboulbenia caprina,L.clavulata,L.cobiae,L.cosmodisci,L.nilotica,L.omalii,L.robusta,L.similis,L.stigmatophora,Laccaria rubriporus,Lasiodiplodia morindae,Lyophyllum agnijum,Marasmiellomycena pseudoomphaliiformis,Melomastia beihaiensis,Nemania guangdongensis,Nigrograna thailandica,Nigrospora ficuum,Oxydothis chinensis,O.yunnanensis,Petriella thailandica,Phaeoacremonium chinensis,Phialocephala chinensis,Phytophthora debattistii,Polyplosphaeria nigrospora,Pronectria loweniae,Seriascoma acutispora,Setoseptoria bambusae,Stictis anomianthi,Tarzetta tibetensis,Tarzetta urceolata,Tetraploa obpyriformis,Trichoglossum beninense,and Tricoderma pyrrosiae.We provide an emendation for Urnula ailaoshanensis Agaricus duplocingulatoides var.brevisporus introduced as a new variety based on morphology and phylogeny.