RepBoTNet-CESA:An Alzheimer’s Disease Computer Aided Diagnosis Method Using Structural Reparameterization BoTNet and Cubic Embedding Self Attention

Various deep learning models have been proposed for the accurate assisted diagnosis of early-stage Alzheimer’s disease(AD).Most studies predominantly employ Convolutional Neural Networks(CNNs),which focus solely on local features,thus encountering difficulties in handling global features.In contrast to natural images,Structural Magnetic Resonance Imaging(sMRI)images exhibit a higher number of channel dimensions.However,during the Position Embedding stage ofMulti Head Self Attention(MHSA),the coded information related to the channel dimension is disregarded.To tackle these issues,we propose theRepBoTNet-CESA network,an advanced AD-aided diagnostic model that is capable of learning local and global features simultaneously.It combines the advantages of CNN networks in capturing local information and Transformer networks in integrating global information,reducing computational costs while achieving excellent classification performance.Moreover,it uses the Cubic Embedding Self Attention(CESA)proposed in this paper to incorporate the channel code information,enhancing the classification performance within the Transformer structure.Finally,the RepBoTNet-CESA performs well in various AD-aided diagnosis tasks,with an accuracy of 96.58%,precision of 97.26%,and recall of 96.23%in the AD/NC task;an accuracy of 92.75%,precision of 92.84%,and recall of 93.18%in the EMCI/NC task;and an accuracy of 80.97%,precision of 83.86%,and recall of 80.91%in the AD/EMCI/LMCI/NC task.This demonstrates that RepBoTNet-CESA delivers outstanding outcomes in various AD-aided diagnostic tasks.Furthermore,our study has shown that MHSA exhibits superior performance compared to conventional attention mechanisms in enhancing ResNet performance.Besides,the Deeper RepBoTNet-CESA network fails to make further progress in AD-aided diagnostic tasks.

Evaluation of computer aided detection during colonoscopy among Veterans:Randomized clinical trial

BACKGROUND There has been significant interest in use of computer aided detection(CADe)devices in colonoscopy to improve polyp detection and reduce miss rate.AIM To investigate the use of CADe amongst veterans.METHODS Between September 2020 and December 2021,we performed a randomized controlled trial to evaluate the impact of CADe.Patients at Veterans Affairs Palo Alto Health Care System presenting for screening or low-risk surveillance were randomized to colonoscopy performed with or without CADe.Primary outcomes of interest included adenoma detection rate(ADR),adenomas per colonoscopy(APC),and adenomas per extraction.In addition,we measured serrated polyps per colonoscopy,non-adenomatous,non-serrated polyps per colonoscopy,serrated polyp detection rate,and procedural time.RESULTS A total of 244 patients were enrolled(124 with CADe),with similar patient characteristics(age,sex,body mass index,indication)between the two groups.Use of CADe was found to have decreased number of adenomas(1.79 vs 2.53,P=0.030)per colonoscopy compared to without CADe.There was no significant difference in number of serrated polyps or non-adenomatous non-serrated polyps per colonoscopy between the two groups.Overall,use of CADe was found to have lower ADR(68.5%vs 80.0%,P=0.041)compared to without use of CADe.Serrated polyp detection rate was lower with CADe(3.2%vs 7.5%)compared to without CADe,but this was not statistically significant(P=0.137).There was no significant difference in withdrawal and procedure times between the two groups or in detection of adenomas per extraction(71.4%vs 73.1%,P=0.613).No adverse events were identified.CONCLUSION While several randomized controlled trials have demonstrated improved ADR and APC with use of CADe,in this RCT performed at a center with high ADR,use of CADe was found to have decreased APC and ADR.Further studies are needed to understand the true impact of CADe on performance quality among endoscopists as well as determine criteria for endoscopists to consider when choosing to adopt CADe in their practices.

Computer aided diabetic retinopathy detection based on ophthalmic photography: a systematic review and Meta-analysis

AIM: To ensure the diagnostic value of computer aided techniques in diabetic retinopathy(DR) detection based on ophthalmic photography(OP). METHODS: PubM ed, EMBASE, Ei village, IEEE Xplore and Cochrane Library database were searched systematically for literatures about computer aided detection(CAD) in DR detection. The methodological quality of included studies was appraised by the Quality Assessment Tool for Diagnostic Accuracy Studies(QUADAS-2). Meta-Di Sc was utilized and a random effects model was plotted to summarize data from those included studies. Summary receiver operating characteristic curves were selected to estimate the overall test performance. Subgroup analysis was used to identify the efficiency of CAD in detecting DR, exudates(EXs), microaneurysms(MAs) as well as hemorrhages(HMs), and neovascularizations(NVs). Publication bias was analyzed using STATA. RESULTS: Fourteen articles were finally included in this Meta-analysis after literature review. Pooled sensitivity and specificity were 90%(95%CI, 85%-94%) and 90%(95%CI, 80%-96%) respectively for CAD in DR detection. With regard to CAD in EXs detecting, pooled sensitivity, specificity were 89%(95%CI, 88%-90%) and99%(95%CI, 99%-99%) respectively. In aspect of MAs and HMs detection, pooled sensitivity and specificity of CAD were 42%(95%CI, 41%-44%) and 93%(95%CI, 93%-93%) respectively. Besides, pooled sensitivity and specificity were 94%(95%CI, 89%-97%) and 87%(95%CI, 83%-90%) respectively for CAD in NVs detection. No potential publication bias was observed. CONCLUSION: CAD demonstrates overall high diagnostic accuracy for detecting DR and pathological lesions based on OP. Further prospective clinical trials are needed to prove such effect.

An Architecture of Computer Aided Process Planning System Integrated with Scheduling Using Decision Support System

Process planning and scheduling are two major plann in g and control activities that consume significant part of the lead-time, theref ore all attempts are being made to reduce lead-time by automating them. Compute r Aided Process Planning (CAPP) is a step in this direction. Most of the existin g CAPP systems do not consider scheduling while generating a process plan. Sched uling is done separately after the process plan has been generated and therefore , it is possible that a process plan so generated is either not optimal or feasi ble from scheduling point of view. As process plans are generated without consid eration of job shop status, many problems arise within the manufacturing environ ment. Investigations have shown that 20%～30% of all process plans generated are not valid and have to be altered or suffer production delays when production sta rts. There is thus a major need for integration of scheduling with computer aide d process planning for generating more realistic process plans. In doing so, eff iciency of the manufacturing system as a whole is expected to improve. Decision support system performs many functions such as selection of machine too ls, cutting tools, sequencing of operations, determination of optimum cutting pa rameters and checking availability of machine tool before allocating any operati on to a machine tool. The process of transforming component data, process capabi lity and decision rules into computer readable format is still a major obstacle. This paper proposes architecture of a system, which integrates computer aided p rocess-planning system with scheduling using decision support system. A decisio n support system can be defined as " an interactive system that provides the use rs with easy access to decision models in order to support semi-structured or u nstructured decision making tasks".

A Computer Aided System for Simulating Weld Metal Solidification Crack

A computer-aided system for simulating weld solidification crack has been developed by which a welding engineer can carry out the welding solidification crack simulation on the basis of a commercial finite element analysis software package. its main functions include calculating the heat generations of the moving arc. mesh generation, calculating stress-strain distributions with element rebirth technique.

Computer Aided Design and Performance Analysis of Inverse Fluidized Bed Biofilm Reactors with Special Reference to Bioplastic Synthesis

Poly Laevo Lactic Acid (PLLA), in spite of being an excellent bioplastic, has exorbitantly high market price due to the high cost of raw material (lactose, glucose, sucrose). Hence, its manufacture is being attempted starting from waste effluents such as cheese whey and molasses. Earlier studies on the same in fluidized bed and semifluidized bed biofilm reactors yielded encouraging results. The present study therefore involves design and analysis of inverse fluidized bed biofilm reactors for lactic acid synthesis. The performance features of the bioreactor have been studied both mathematically as well as experimentally. The inverse fluidized bed biofilm reactor has been found to provide more than 75% conversion of sucrose/lactose even at high capacities (high feed flow rates) exceeding 56,000 L/hr, within a reasonably low reactor volume. The fractional substrate conversion increases, though sluggishly, with increase in feed flow rate due to bed expansion and also with increase in cell mass concentration in biofilm due to enhancement in intrinsic rate of bioconversion. The inverse fluidized bed biofilm reactor of proposed design could be safely recommended for the commercial synthesis of polymer grade lactic acid from waste effluents such as cheese whey and molasses. The low operating cost of the bioreactor (due to downflow mode of operation) enhances the economy of the process. This would also help in significantly lowering the market price of the green plastic (PLLA) and shall promote its large scale manufacture and utilisation.

A Brief Analysis of the Translator’s Subjectivity in the Process of Computer Aided Translation

In the era of globalization and big data,the demand for translation is growing rapidly,and the task of translators is be⁃coming increasingly burdensome.The emergence of computer aided translation technology provides great convenience for transla⁃tion work.The main body of computer aided translation process is the translator,which avoids many drawbacks in machine transla⁃tion.This paper aims to have a brief analysis of the translator's subjectivity in the process of computer aided translation,which will be helpful for the development of the computer aided translation technology.

Application of computer aided 3D simulation technique for complicated foot and ankle fractures

Objective To investigate the effect of computer aided 3D simulation technique for treating complicated foot and ankle fractures precisely.Methods From November 2007 to August 2009,255 patients with complicated foot and ankle fractures

Artificial intelligence for characterization of diminutive colorectal polyps:A feasibility study comparing two computer-aided diagnosis systems

BACKGROUND Artificial intelligence(AI)has potential in the optical diagnosis of colorectal polyps.AIM To evaluate the feasibility of the real-time use of the computer-aided diagnosis system(CADx)AI for ColoRectal Polyps(AI4CRP)for the optical diagnosis of diminutive colorectal polyps and to compare the performance with CAD EYE^(TM)(Fujifilm,Tokyo,Japan).CADx influence on the optical diagnosis of an expert endoscopist was also investigated.METHODS AI4CRP was developed in-house and CAD EYE was proprietary software provided by Fujifilm.Both CADxsystems exploit convolutional neural networks.Colorectal polyps were characterized as benign or premalignant and histopathology was used as gold standard.AI4CRP provided an objective assessment of its characterization by presenting a calibrated confidence characterization value(range 0.0-1.0).A predefined cut-off value of 0.6 was set with values<0.6 indicating benign and values≥0.6 indicating premalignant colorectal polyps.Low confidence characterizations were defined as values 40%around the cut-off value of 0.6(<0.36 and>0.76).Self-critical AI4CRP’s diagnostic performances excluded low confidence characterizations.RESULTS AI4CRP use was feasible and performed on 30 patients with 51 colorectal polyps.Self-critical AI4CRP,excluding 14 low confidence characterizations[27.5%(14/51)],had a diagnostic accuracy of 89.2%,sensitivity of 89.7%,and specificity of 87.5%,which was higher compared to AI4CRP.CAD EYE had a 83.7%diagnostic accuracy,74.2%sensitivity,and 100.0%specificity.Diagnostic performances of the endoscopist alone(before AI)increased nonsignificantly after reviewing the CADx characterizations of both AI4CRP and CAD EYE(AI-assisted endoscopist).Diagnostic performances of the AI-assisted endoscopist were higher compared to both CADx-systems,except for specificity for which CAD EYE performed best.CONCLUSION Real-time use of AI4CRP was feasible.Objective confidence values provided by a CADx is novel and self-critical AI4CRP showed higher diagnostic performances compared to AI4CRP.

A Method for Determining Surface Free Energy of Bamboo Fiber Materials by Applying Fowkes Theory and Using Computer Aided Machine Vision Based Measurement Technique

The purpose of this study is to develop a standard methodology for measuring the surface free energy (SFE),and its component parts of bamboo fiber materials.The current methods was reviewed to determine the surface tension of natural fibers and the disadvantages of techniques used were discussed.Although numerous techniques have been employed to characterize surface tension of natural fibers,it seems that the credibility of results obtained may often be dubious.In this paper,critical surface tension estimates were obtained from computer aided machine vision based measurement.Data were then analyzed by the least squares method to estimate the components of SFE.SFE was estimated by least squares analysis and also by Schultz' method.By using the Fowkes method the polar and disperse fractions of the surface free energy of bamboo fiber materials can be obtained.Strictly speaking,this method is based on a combination of the knowledge of Fowkes theory. SFE is desirable when adhesion is required,and it avoids some of the limitations of existing studies which has been proposed.The calculation steps described in this research are only intended to explain the methods.The results show that the method that only determines SFE as a single parameter may be unable to differentiate adequately between bamboo fiber materials,but it is feasible and very efficient.In order to obtain the maximum performance from the computer aided machine vision based measurement instruments,this measurement should be recommended and kept available for reference.

Recent advances in computerized imaging and its vital roles in liverdisease diagnosis, preoperative planning, and interventional liversurgery: A review

The earliest and most accurate detection of the pathological manifestations of hepatic diseases ensures effective treatments and thus positive prognostic outcomes.In clinical settings,screening and determining the extent of a pathology are prominent factors in preparing remedial agents and administering approp-riate therapeutic procedures.Moreover,in a patient undergoing liver resection,a realistic preoperative simulation of the subject-specific anatomy and physiology also plays a vital part in conducting initial assessments,making surgical decisions during the procedure,and anticipating postoperative results.Conventionally,various medical imaging modalities,e.g.,computed tomography,magnetic resonance imaging,and positron emission tomography,have been employed to assist in these tasks.In fact,several standardized procedures,such as lesion detection and liver segmentation,are also incorporated into prominent commercial software packages.Thus far,most integrated software as a medical device typically involves tedious interactions from the physician,such as manual delineation and empirical adjustments,as per a given patient.With the rapid progress in digital health approaches,especially medical image analysis,a wide range of computer algorithms have been proposed to facilitate those procedures.They include pattern recognition of a liver,its periphery,and lesion,as well as pre-and postoperative simulations.Prior to clinical adoption,however,software must conform to regulatory requirements set by the governing agency,for instance,valid clinical association and analytical and clinical validation.Therefore,this paper provides a detailed account and discussion of the state-of-the-art methods for liver image analyses,visualization,and simulation in the literature.Emphasis is placed upon their concepts,algorithmic classifications,merits,limitations,clinical considerations,and future research trends.

Improving Thyroid Disorder Diagnosis via Ensemble Stacking and Bidirectional Feature Selection

Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid gland.Accurate and timely diagnosis of these disorders is crucial for effective treatment and patient care.This research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection techniques.Sequential forward feature selection,sequential backward feature elimination,and bidirectional feature elimination are investigated in this study.In ensemble learning,random forest,adaptive boosting,and bagging classifiers are employed.The effectiveness of these techniques is evaluated using two different datasets obtained from the University of California Irvine-Machine Learning Repository,both of which undergo preprocessing steps,including outlier removal,addressing missing data,data cleansing,and feature reduction.Extensive experimentation demonstrates the remarkable success of proposed ensemble stacking and bidirectional feature elimination achieving 100%and 99.86%accuracy in identifying hyperthyroidism and hypothyroidism,respectively.Beyond enhancing detection accuracy,the ensemble stacking model also demonstrated a streamlined computational complexity which is pivotal for practical medical applications.It significantly outperformed existing studies with similar objectives underscoring the viability and effectiveness of the proposed scheme.This research offers an innovative perspective and sets the platform for improved thyroid disorder diagnosis with broader implications for healthcare and patient well-being.

CATIA AIDED RADOME ANALYSIS USING GEOMETRIC OPTICS METHOD

In this paper, the Geometric Optics (GO) method using the approximate ray paths coupled with the Computer Aided Tri-dimensional Interface Application (CATIA) meshing modeling are implemented to analyze the performance of electric large three-dimensional dielectric radome-enclosed antenna of arbitrary contour shape. The surfaces of the radome are approximated by planar triangular patches, the influences of various number of patches on power transmission coefficient and Insertion Phase Delay (IPD) via an ogive and a conical radome are discussed by the hybrid method. The simulation results indicate that computational error from planar triangular patches can limit in one percent, meeting the engineering application requirements.

Symbiotic Organisms Search with Deep Learning Driven Biomedical Osteosarcoma Detection and Classification

Osteosarcoma is one of the rare bone cancers that affect the individualsaged between 10 and 30 and it incurs high death rate. Early diagnosisof osteosarcoma is essential to improve the survivability rate and treatmentprotocols. Traditional physical examination procedure is not only a timeconsumingprocess, but it also primarily relies upon the expert’s knowledge.In this background, the recently developed Deep Learning (DL) models canbe applied to perform decision making. At the same time, hyperparameteroptimization of DL models also plays an important role in influencing overallclassification performance. The current study introduces a novel SymbioticOrganisms Search with Deep Learning-driven Osteosarcoma Detection andClassification (SOSDL-ODC) model. The presented SOSDL-ODC techniqueprimarily focuses on recognition and classification of osteosarcoma usinghistopathological images. In order to achieve this, the presented SOSDL-ODCtechnique initially applies image pre-processing approach to enhance the qualityof image. Also, MobileNetv2 model is applied to generate a suitable groupof feature vectors whereas hyperparameter tuning of MobileNetv2 modelis performed using SOS algorithm. At last, Gated Recurrent Unit (GRU)technique is applied as a classification model to determine proper class labels.In order to validate the enhanced osteosarcoma classification performance ofthe proposed SOSDL-ODC technique, a comprehensive comparative analysiswas conducted. The obtained outcomes confirmed the betterment of SOSDLODCapproach than the existing approaches as the former achieved a maximumaccuracy of 97.73%.

Arithmetic Optimization with Ensemble Deep Transfer Learning Based Melanoma Classification

Melanoma is a skin disease with high mortality rate while earlydiagnoses of the disease can increase the survival chances of patients. Itis challenging to automatically diagnose melanoma from dermoscopic skinsamples. Computer-Aided Diagnostic (CAD) tool saves time and effort indiagnosing melanoma compared to existing medical approaches. In this background,there is a need exists to design an automated classification modelfor melanoma that can utilize deep and rich feature datasets of an imagefor disease classification. The current study develops an Intelligent ArithmeticOptimization with Ensemble Deep Transfer Learning Based MelanomaClassification (IAOEDTT-MC) model. The proposed IAOEDTT-MC modelfocuses on identification and classification of melanoma from dermoscopicimages. To accomplish this, IAOEDTT-MC model applies image preprocessingat the initial stage in which Gabor Filtering (GF) technique is utilized.In addition, U-Net segmentation approach is employed to segment the lesionregions in dermoscopic images. Besides, an ensemble of DL models includingResNet50 and ElasticNet models is applied in this study. Moreover, AOalgorithm with Gated Recurrent Unit (GRU) method is utilized for identificationand classification of melanoma. The proposed IAOEDTT-MC methodwas experimentally validated with the help of benchmark datasets and theproposed model attained maximum accuracy of 92.09% on ISIC 2017 dataset.

Sailfish Optimization with Deep Learning Based Oral Cancer Classification Model

Recently,computer aided diagnosis(CAD)model becomes an effective tool for decision making in healthcare sector.The advances in computer vision and artificial intelligence(AI)techniques have resulted in the effective design of CAD models,which enables to detection of the existence of diseases using various imaging modalities.Oral cancer(OC)has commonly occurred in head and neck globally.Earlier identification of OC enables to improve survival rate and reduce mortality rate.Therefore,the design of CAD model for OC detection and classification becomes essential.Therefore,this study introduces a novel Computer Aided Diagnosis for OC using Sailfish Optimization with Fusion based Classification(CADOC-SFOFC)model.The proposed CADOC-SFOFC model determines the existence of OC on the medical images.To accomplish this,a fusion based feature extraction process is carried out by the use of VGGNet-16 and Residual Network(ResNet)model.Besides,feature vectors are fused and passed into the extreme learning machine(ELM)model for classification process.Moreover,SFO algorithm is utilized for effective parameter selection of the ELM model,consequently resulting in enhanced performance.The experimental analysis of the CADOC-SFOFC model was tested on Kaggle dataset and the results reported the betterment of the CADOC-SFOFC model over the compared methods with maximum accuracy of 98.11%.Therefore,the CADOC-SFOFC model has maximum potential as an inexpensive and non-invasive tool which supports screening process and enhances the detection efficiency.

Horizontal Voting Ensemble Based Predictive Modeling System for Colon Cancer

Colon cancer is the third most commonly diagnosed cancer in the world.Most colon AdenoCArcinoma(ACA)arises from pre-existing benign polyps in the mucosa of the bowel.Thus,detecting benign at the earliest helps reduce the mortality rate.In this work,a Predictive Modeling System(PMS)is developed for the classification of colon cancer using the Horizontal Voting Ensemble(HVE)method.Identifying different patterns inmicroscopic images is essential to an effective classification system.A twelve-layer deep learning architecture has been developed to extract these patterns.The developedHVE algorithm can increase the system’s performance according to the combined models from the last epochs of the proposed architecture.Ten thousand(10000)microscopic images are taken to test the classification performance of the proposed PMS with the HVE method.The microscopic images obtained from the colon tissues are classified intoACAor benign by the proposed PMS.Results prove that the proposed PMS has∼8%performance improvement over the architecture without using the HVE method.The proposed PMS for colon cancer reduces the misclassification rate and attains 99.2%of sensitivity and 99.4%of specificity.The overall accuracy of the proposed PMS is 99.3%,and without using the HVE method,it is only 91.3%.

Automated Skin Lesion Diagnosis and Classification Using Learning Algorithms

Due to the rising occurrence of skin cancer and inadequate clinical expertise,it is needed to design Artificial Intelligence(AI)based tools to diagnose skin cancer at an earlier stage.Since massive skin lesion datasets have existed in the literature,the AI-based Deep Learning(DL)modelsfind useful to differentiate benign and malignant skin lesions using dermoscopic images.This study develops an Automated Seeded Growing Segmentation with Optimal EfficientNet(ARGS-OEN)technique for skin lesion segmentation and classification.The proposed ASRGS-OEN technique involves the design of an optimal EfficientNet model in which the hyper-parameter tuning process takes place using the Flower Pollination Algorithm(FPA).In addition,Multiwheel Attention Memory Network Encoder(MWAMNE)based classification technique is employed for identifying the appropriate class labels of the dermoscopic images.A comprehensive simulation analysis of the ASRGS-OEN technique takes place and the results are inspected under several dimensions.The simulation results highlighted the supremacy of the ASRGS-OEN technique on the applied dermoscopic images compared to the recently developed approaches.

Deep Learning with Optimal Hierarchical Spiking Neural Network for Medical Image Classification

Medical image classification becomes a vital part of the design of computer aided diagnosis(CAD)models.The conventional CAD models are majorly dependent upon the shapes,colors,and/or textures that are problem oriented and exhibited complementary in medical images.The recently developed deep learning(DL)approaches pave an efficient method of constructing dedicated models for classification problems.But the maximum resolution of medical images and small datasets,DL models are facing the issues of increased computation cost.In this aspect,this paper presents a deep convolutional neural network with hierarchical spiking neural network(DCNN-HSNN)for medical image classification.The proposed DCNN-HSNN technique aims to detect and classify the existence of diseases using medical images.In addition,region growing segmentation technique is involved to determine the infected regions in the medical image.Moreover,NADAM optimizer with DCNN based Capsule Network(CapsNet)approach is used for feature extraction and derived a collection of feature vectors.Furthermore,the shark smell optimization algorithm(SSA)based HSNN approach is utilized for classification process.In order to validate the better performance of the DCNN-HSNN technique,a wide range of simulations take place against HIS2828 and ISIC2017 datasets.The experimental results highlighted the effectiveness of the DCNN-HSNN technique over the recent techniques interms of different measures.Please type your abstract here.