Significance of ROI Coding using MAXSHIFT Scaling applied on MRI Images in Teleradiology-Telemedicine

Within the expanding paradigm of medical imaging in Teleradiology-Telemedicine there is increasing demand for transmitting diagnostic medical imagery. These are usually rich in radiological contents and the associated file sizes are large which must be compressed with minimal file size to minimize transmission time and robustly coded to withstand required network medium. It has been reinforced through extensive research that the diagnostically important regions of medical images, the Region of Interest (ROI), must be compressed by lossless or near lossless algorithm while on the other hand, the background region be compressed with some loss of information but still recognizable using JPEG 2000 standard. We develop a compression model and present its application on MRI images. Applying on MRI images achieved higher compression ratio 16:1, analogously minimum transmission time, using MAXSHIFT method proved diagnostically significant and effective both objectively and subjectively.

Effectiveness of Deep Learning Models for Brain Tumor Classification and Segmentation

A brain tumor is a mass or growth of abnormal cells in the brain.In children and adults,brain tumor is considered one of the leading causes of death.There are several types of brain tumors,including benign(non-cancerous)and malignant(cancerous)tumors.Diagnosing brain tumors as early as possible is essential,as this can improve the chances of successful treatment and survival.Considering this problem,we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models(Resnet50,Vgg16,Vgg19,U-Net)and their integration for computer-aided detection and localization systems in brain tumors.These pre-trained and integrated deep learning models have been used on the publicly available dataset from The Cancer Genome Atlas.The dataset consists of 120 patients.The pre-trained models have been used to classify tumor or no tumor images,while integrated models are applied to segment the tumor region correctly.We have evaluated their performance in terms of loss,accuracy,intersection over union,Jaccard distance,dice coefficient,and dice coefficient loss.From pre-trained models,the U-Net model achieves higher performance than other models by obtaining 95%accuracy.In contrast,U-Net with ResNet-50 out-performs all other models from integrated pre-trained models and correctly classified and segmented the tumor region.

A U-Net-Based CNN Model for Detection and Segmentation of Brain Tumor

Human brain consists of millions of cells to control the overall structure of the human body.When these cells start behaving abnormally,then brain tumors occurred.Precise and initial stage brain tumor detection has always been an issue in the field of medicines for medical experts.To handle this issue,various deep learning techniques for brain tumor detection and segmentation techniques have been developed,which worked on different datasets to obtain fruitful results,but the problem still exists for the initial stage of detection of brain tumors to save human lives.For this purpose,we proposed a novel U-Net-based Convolutional Neural Network(CNN)technique to detect and segmentizes the brain tumor for Magnetic Resonance Imaging(MRI).Moreover,a 2-dimensional publicly available Multimodal Brain Tumor Image Segmentation(BRATS2020)dataset with 1840 MRI images of brain tumors has been used having an image size of 240×240 pixels.After initial dataset preprocessing the proposed model is trained by dividing the dataset into three parts i.e.,testing,training,and validation process.Our model attained an accuracy value of 0.98%on the BRATS2020 dataset,which is the highest one as compared to the already existing techniques.

A Novel-based Swin Transfer Based Diagnosis of COVID-19 Patients

The numbers of cases and deaths due to the COVID-19 virus have increased daily all around the world.Chest X-ray is considered very useful and less time-consuming for monitoring COVID disease.No doubt,X-ray is considered as a quick screening method,but due to variations in features of images which are of X-rays category with Corona confirmed cases,the domain expert is needed.To address this issue,we proposed to utilize deep learning approaches.In this study,the dataset of COVID-19,lung opacity,viral pneumonia,and lastly healthy patients’images of category X-rays are utilized to evaluate the performance of the Swin transformer for predicting the COVID-19 patients efficiently.The performance of the Swin transformer is compared with the other seven deep learning models,including ResNet50,DenseNet121,InceptionV3,EfficientNetB2,VGG19,ViT,CaIT,Swim transformer provides 98%recall and 96%accuracy on corona affected images of the X-ray category.The proposed approach is also compared with state-of-the-art techniques for COVID-19 diagnosis,and proposed technique is found better in terms of accuracy.Our system could support clin-icians in screening patients for COVID-19,thus facilitating instantaneous treatment for better effects on the health of COVID-19 patients.Also,this paper can contribute to saving humanity from the adverse effects of trials that the Corona virus might bring by performing an accurate diagnosis over Corona-affected patients.

Multi-Level Deep Generative Adversarial Networks for Brain Tumor Classification on Magnetic Resonance Images

The brain tumor is an abnormal and hysterical growth of brain tissues,and the leading cause of death affected patients worldwide.Even in this technol-ogy-based arena,brain tumor images with proper labeling and acquisition still have a problem with the accurate and reliable generation of realistic images of brain tumors that are completely different from the original ones.The artificially created medical image data would help improve the learning ability of physicians and other computer-aided systems for the generation of augmented data.To over-come the highlighted issue,a Generative Adversarial Network(GAN)deep learn-ing technique in which two neural networks compete to become more accurate in creating artificially realistic data for MRI images.The GAN network contains mainly two parts known as generator and discriminator.Commonly,a generator is the convolutional neural network,and a discriminator is the deconvolutional neural network.In this research,the publicly accessible Contrast-Enhanced Mag-netic Resonance Imaging(CE-MRI)dataset collected from 2005-to 2020 from different hospitals in China consists of four classes has been used.Our proposed method is simple and achieved an accuracy of 96%.We compare our technique results with the existing results,indicating that our proposed technique outper-forms the best results associated with the existing methods.

Aspect-Based Sentiment Analysis for Social Multimedia:A Hybrid Computational Framework

People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events,public products and the latest affairs.People share their thoughts and feelings about various topics,including products,news,blogs,etc.In user reviews and tweets,sentiment analysis is used to discover opinions and feelings.Sentiment polarity is a term used to describe how sentiment is represented.Positive,neutral and negative are all examples of it.This area is still in its infancy and needs several critical upgrades.Slang and hidden emotions can detract from the accuracy of traditional techniques.Existing methods only evaluate the polarity strength of the sentiment words when dividing them into positive and negative categories.Some existing strategies are domain-specific.The proposed model incorporates aspect extraction,association rule mining and the deep learning technique Bidirectional EncoderRepresentations from Transformers(BERT).Aspects are extracted using Part of Speech Tagger and association rulemining is used to associate aspects with opinion words.Later,classification was performed using BER.The proposed approach attained an average of 89.45%accuracy,88.45%precision and 85.98%recall on different datasets of products and Twitter.The results showed that the proposed technique achieved better than state-of-the-art sentiment analysis techniques.

慢性心力衰竭患者血浆心脏间质成分的水平及其相关性

目的:观察慢性心力衰竭(心衰)患者血浆金属蛋白酶(MMP)1,2及Ⅰ型、Ⅲ型前胶原氨基端肽,血管紧张素Ⅱ和醛固酮的水平。探讨这些成分与心衰发生以及相互之间的联系。方法:对86例慢性心衰患者应用双抗体夹心法检测血清MMP1、MMP2水平。放射免疫法检测其PⅠNP、PⅢNP、AngⅡ及ALD的水平。并与60例健康对照组进行对比研究。结果:(1)慢性心衰患者血浆MMP1、PⅠNP、PⅢNP、AngⅡ和ALD水平均高于正常对照组(P<0.01)。(2)偏相关分析显示MMP1与MMP2、PⅠNP与PⅢNP、AngⅡ与ALD、PⅢNP与AngⅡ之间有正相关关系(P<0.05)。结论:心脏间质成分MMP1、PⅠNP、PⅢNP及调节因子AngⅡ和ALD在心衰患者血浆中的水平升高,且不同间质成分之间存在着相互影响的关系。

Breast Cancer Detection in Saudi Arabian Women Using Hybrid Machine Learning on Mammographic Images

Breast cancer(BC)is the most common cause of women’s deaths worldwide.The mammography technique is the most important modality for the detection of BC.To detect abnormalities in mammographic images,the Breast Imaging Reporting and Data System(BI-RADs)is used as a baseline.The correct allocation of BI-RADs categories for mammographic images is always an interesting task,even for specialists.In this work,to detect and classify the mammogram images in BI-RADs,a novel hybrid model is presented using a convolutional neural network(CNN)with the integration of a support vector machine(SVM).The dataset used in this research was collected from different hospitals in the Qassim health cluster of Saudi Arabia.The collection of all categories of BI-RADs is one of the major contributions of this paper.Another significant contribution is the development of a hybrid approach through the integration of CNN and SVM.The proposed hybrid approach uses three CNN models to obtain ensemble CNN model results.This ensemble model saves the values to integrate them with SVM.The proposed system achieved a classification accuracy,sensitivity,specificity,precision,and F1-score of 93.6%,94.8%,96.9%,96.6%,and 95.7%,respectively.The proposed model achieved better performance compared to previously available methods.

A Novel Inherited Modeling Structure of Automatic Brain Tumor Segmentation from MRI

Brain tumor is one of the most dreadful worldwide types of cancer and affects people leading to death.Magnetic resonance imaging methods capture skull images that contain healthy and affected tissue.Radiologists checked the affected tissue in the slice-by-slice manner,which was timeconsuming and hectic task.Therefore,auto segmentation of the affected part is needed to facilitate radiologists.Therefore,we have considered a hybrid model that inherits the convolutional neural network(CNN)properties to the support vector machine(SVM)for the auto-segmented brain tumor region.The CNN model is initially used to detect brain tumors,while SVM is integrated to segment the tumor region correctly.The proposed method was evaluated on a publicly available BraTS2020 dataset.The statistical parameters used in this work for the mathematical measures are precision,accuracy,specificity,sensitivity,and dice coefficient.Overall,our method achieved an accuracy value of 0.98,which is most prominent than existing techniques.Moreover,the proposed approach is more suitable for medical experts to diagnose the early stages of the brain tumor.

Block-Wise Neural Network for Brain Tumor Identification in Magnetic Resonance Images

The precise brain tumor diagnosis is critical and shows a vital role in the medical support for treating tumor patients.Manual brain tumor segmentation for cancer analysis from many Magnetic Resonance Images(MRIs)created in medical practice is a problematic and timewasting task for experts.As a result,there is a critical necessity for more accurate computeraided methods for early tumor detection.To remove this gap,we enhanced the computational power of a computer-aided system by proposing a finetuned Block-Wise Visual Geometry Group19(BW-VGG19)architecture.In this method,a pre-trained VGG19 is fine-tuned with CNN architecture in the block-wise mechanism to enhance the system`s accuracy.The publicly accessible Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)dataset collected from 2005 to 2020 from different hospitals in China has been used in this research.Our proposed method is simple and achieved an accuracy of 0.98%.We compare our technique results with the existing Convolutional Neural network(CNN),VGG16,and VGG19 approaches.The results indicate that our proposed technique outperforms the best results associated with the existing methods.

Automated Speech Recognition System to Detect Babies’ Feelings through Feature Analysis

Diagnosing a baby’s feelings poses a challenge for both doctors and parents because babies cannot explain their feelings through expression or speech.Understanding the emotions of babies and their associated expressions during different sensations such as hunger,pain,etc.,is a complicated task.In infancy,all communication and feelings are propagated through cryspeech,which is a natural phenomenon.Several clinical methods can be used to diagnose a baby’s diseases,but nonclinical methods of diagnosing a baby’s feelings are lacking.As such,in this study,we aimed to identify babies’feelings and emotions through their cry using a nonclinical method.Changes in the cry sound can be identified using our method and used to assess the baby’s feelings.We considered the frequency of the cries from the energy of the sound.The feelings represented by the infant’s cry are judged to represent certain sensations expressed by the child using the optimal frequency of the recognition of a real-world audio sound.We used machine learning and artificial intelligence to distinguish cry tones in real time through feature analysis.The experimental group consisted of 50%each male and female babies,and we determined the relevancy of the results against different parameters.This application produced real-time results after recognizing a child’s cry sounds.The novelty of our work is that we,for the first time,successfully derived the feelings of young children through the cry-speech of the child,showing promise for end-user applications.

A Novel Hybrid Machine Learning Approach for Classification of Brain Tumor Images

Abnormal growth of brain tissues is the real cause of brain tumor.Strategy for the diagnosis of brain tumor at initial stages is one of the key step for saving the life of a patient.The manual segmentation of brain tumor magnetic resonance images(MRIs)takes time and results vary significantly in low-level features.To address this issue,we have proposed a ResNet-50 feature extractor depended on multilevel deep convolutional neural network(CNN)for reliable images segmentation by considering the low-level features of MRI.In this model,we have extracted features through ResNet-50 architecture and fed these feature maps to multi-level CNN model.To handle the classification process,we have collected a total number of 2043 MRI patients of normal,benign,and malignant tumor.Three model CNN,multi-level CNN,and ResNet-50 based multi-level CNN have been used for detection and classification of brain tumors.All the model results are calculated in terms of various numerical values identified as precision(P),recall(R),accuracy(Acc)and f1-score(F1-S).The obtained average results are much better as compared to already existing methods.This modified transfer learning architecture might help the radiologists and doctors as a better significant system for tumor diagnosis.

Progress in 3D‑MXene Electrodes for Lithium/Sodium/Potassium/Magnesium/Zinc/Aluminum‑Ion Batteries

MXenes have attracted increasing attention because of their rich surface functional groups,high electrical conductivity,and outstanding dispersibility in many solvents,and have demonstrated competitive efficiency in energy storage and conversion applications.However,the restacking nature of MXene nanosheets like other two-dimensional(2D)materials through van der Waals forces results in sluggish ionic kinetics,restricted number of active sites,and ultimate deterioration of MXene mate-rial/device performance.The strategy of raising 2D MXenes into three-dimensional(3D)structures has been considered an efficient way for reducing restacking,providing greater porosity,higher surface area,and shorter distances for mass transport of ions,surpassing standard one-dimensional(1D)and 2D structures.In multivalent ion batteries,the positive multivalent ions combine with two or more electrons at the same time,so their capacities are two or three times that of lithium-ion batteries(LIBs)under the same conditions,e.g.,a magnesium ion battery has a high theoretical specific capacity of 2205 mAh g^(−1)and a high volumetric capacity of 3833 mAh cm^(−3).In this review,we summarize the most recent strategies for fabricating 3D MXene architectures,such as assembly,template,3D printing,electrospinning,aerogel,and gas foaming methods.Special consideration has been given to the applications of highly porous 3D MXenes in energy storage devices beyond LIBs,such as sodium ion batteries(SIBs),potassium ion batteries(KIBs),magnesium ion batteries(MIBs),zinc ion batteries(ZIBs),and aluminum ion batteries(AIBs).Finally,the authors provide a summary of the future opportunities and challenges for the construction of 3D MXenes and MXene-based electrodes for applications beyond LIBs.

全球价值链视角下中国农产品贸易隐含氮、磷、钾研究

基于GTAP数据库提供的多区域投入产出表,采用全球价值链方法测算中国农产品贸易中隐含的化肥转移,并按照农产品最终消费的地理位置将其分解为四部分。研究发现:(1)中国农产品进口为国内节约640万t化肥(占我国化肥用量的13%),同时引起全球化肥用量节约285万t,为缓解全球资源和环境压力做出贡献;(2)中国农产品贸易深度参与全球价值链,进口农产品中隐含的化肥有12%会再次出口到全球,意味着农产品贸易背后隐含的资源到达中国后会再次出口,形成多次跨境转移。建议在全球价值链视角下更为客观地估算农产品贸易隐含的化肥及其他资源环境问题,倡导共同承担贸易引致的资源环境问题。

Unravelling critical role of metal cation engineering in boosting hydrogen evolution reaction activity of molybdenum diselenide

Two-dimensional(2D)transition-metal selenides,espe-cially MoSe_(2),is considered to be an excellent alternative electrocatalyst for the hydrogen evolution reaction(HER).However,it still features high overpotential in HER due to the low density of active sites,which limits its practical application.Herein,the hydrogen evolution reaction activity of MoSe_(2)is enhanced by the incorporation of metal-cation,tungsten,which succeeds in taking the place of Mo in the lattice of MoSe_(2),inducing the spacing expansion and bringing new flexural edges to serve as active sites.In addition,the incorporated metal also facil-itates electron transport from Mo active center toward W and Se atoms with auspicious hydrogen adsorption prop-erties.