Diagnosis Of Systemic Diseases Research Articles

Privacy-Preserving and Verifiable Cloud-Aided Disease Diagnosis and Prediction With Hyperplane Decision-Based Classifier

With the vigorous development and gradual maturity of machine learning (ML) technologies, the AI-assisted disease diagnosis and prediction ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {AADP}$ </tex-math></inline-formula> ) system has been extensively studied and can be expected to be intensively deployed in the real world. However, as the scale of ML data increases exponentially, the training and application of ML models impose a great burden on resource-constrained terminals. Designing cloud/edge server-aided <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {AADP}$ </tex-math></inline-formula> protocols is becoming a popular topic. Whereas, the sensitivity of ML data, the intellectual property of ML models, and the uncontrollability of servers bring great security challenges to this promising computing paradigm. In this article, we initialize a new four-party framework for the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {AADP}$ </tex-math></inline-formula> system which consists of users, third-party test institution, AI doctor, and cloud/edge server. With this framework, we design two high-efficiency and secure outsourcing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {AADP}$ </tex-math></inline-formula> protocols under two different security models. By comprehensively employing secure hash functions, Householder transformations, and random permutations, we realize the following design objectives: 1) user’s actual identification is invisible to the other parties; 2) user’s feature vector is blinded to the AI doctor and the server; 3) the ML model of the AI doctor is confidential to the server; 4) AI doctor can obtain decent computational savings compared with achieving the diagnosis task by itself; and 5) AI doctor can verify the server’s misbehaviors with a nonnegligible probability under the security model with a fully malicious server. We argue these claims with rigid theoretical proofs and corroborate them with extensive experimental analysis.

Dual Attention Multiscale Network for Vessel Segmentation in Fundus Photography

Automatic vessel structure segmentation is essential for an automatic disease diagnosis system. The task is challenging due to vessels’ different shapes and sizes across populations. This paper proposes a multiscale network with dual attention to segment various retinal blood vessels. The network injects a spatial attention module and channel attention module on a feature map, whose size is one-eighth of the input size. The network also uses multiscale input to receive multi-level information, and the network uses the multiscale output to gain more supervision. The proposed method is tested on two publicly available datasets: DRIVE and CHASEDB1. The accuracy, AUC, sensitivity, and specificity on the DRIVE dataset are 0.9615, 0.9866, 0.7709, and 0.9847, respectively. On the CHASEDB1 dataset, the metrics are 0.9800, 0.9892, 0.8215, and 0.9877, respectively. The ablative study further shows effectiveness for each part of the network. Multiscale and dual attention mechanism both improve performance. The proposed architecture is simple and effective. The inference time is 12 ms on a GPU and has potential for real-world applications. The code will be made publicly available.

A priority queue-based telemonitoring system for automatic diagnosis of heart diseases in integrated fog computing environments.

Various studies have shown the benefits of using distributed fog computing for healthcare systems. The new pattern of fog and edge computing reduces latency for data processing compared to cloud computing. Nevertheless, the proposed fog models still have many limitations in improving system performance and patients' response time.This paper, proposes a new performance model by integrating fog computing, priority queues and certainty theory into the Edge computing devices and validating it by analyzing heart disease patients' conditions in clinical decision support systems (CDSS). In this model, a Certainty Factor (CF) value is assigned to each symptom of heart disease. When one or more symptoms show an abnormal value, the patient's condition will be evaluated using CF values in the fog layer. In the fog layer, requests are categorized in different priority queues before arriving into the system. The results demonstrate that network usage, latency, and response time of patients' requests are respectively improved by 25.55%, 42.92%, and 34.28% compared to the cloud model. Prioritizing patient requests with respect to CF values in the CDSS provides higher system Quality of Service (QoS) and patients' response time.

SISTEM PAKAR DIAGNOSIS HAMA DAN PENYAKIT PADA TANAMAN KANGKUNG MENGGUNAKAN METODE FORWARD CHAINING

The production of water spinach vegetable crops in 2020 in Central Java ranks 2nd in water spinach production, which is 29,108 tons. The amount of water in spinach production and the increased risk of being attacked by pests and diseases will be even more significant, and proper care is needed to improve the quality of water spinach. It takes an expert to convey knowledge about controlling pests and diseases that are good and right. From these problems, we developed a system to overcome the problem of water spinach plant disease. This expert system uses the forward chaining method. The results of this study are a pest and disease diagnosis system on water spinach using a website-based forward chaining method with accuracy testing using the confusion matrix method by testing 50 rules. Of 50 data, 45 are considered appropriate, and the results of 5 rules are not relevant, so the evaluation results of this expert system produce system accuracy with a percentage of 90%.

A Modified LBP Operator-Based Optimized Fuzzy Art Map Medical Image Retrieval System for Disease Diagnosis and Prediction.

Medical records generated in hospitals are treasures for academic research and future references. Medical Image Retrieval (MIR) Systems contribute significantly to locating the relevant records required for a particular diagnosis, analysis, and treatment. An efficient classifier and effective indexing technique are required for the storage and retrieval of medical images. In this paper, a retrieval framework is formulated by adopting a modified Local Binary Pattern feature (AvN-LBP) for indexing and an optimized Fuzzy Art Map (FAM) for classifying and searching medical images. The proposed indexing method extracts LBP considering information from neighborhood pixels and is robust to background noise. The FAM network is optimized using the Differential Evaluation (DE) algorithm (DEFAMNet) with a modified mutation operation to minimize the size of the network without compromising the classification accuracy. The performance of the proposed DEFAMNet is compared with that of other classifiers and descriptors; the classification accuracy of the proposed AvN-LBP operator with DEFAMNet is higher. The experimental results on three benchmark medical image datasets provide evidence that the proposed framework classifies the medical images faster and more efficiently with lesser computational cost.

Vision-based gait analysis for real-time Parkinson disease identification and diagnosis system

ABSTRACT Computer-assisted Parkinson’s disease-specific gait pattern recognition has gained more attention in the past decade due to its extensive application. In this research study, vision-based gait feature extraction is obtained from the observed skeleton points to support the real-time Parkinson disease prediction and diagnosis in the smart healthcare environment. So, a novel kernel-based principal component analysis (KPCA) is introduced for establishing respective feature extraction and dimensionality reduction on the patient’s video data. In this research study, a vision-based Parkinson disease identification system (VPDIS) is developed with a feature-weighted minimum distance classifier model to support the clinical assessment of Parkinson’s disease. At the time of experimentation, a steady-state walking style of the patient was captured using the cameras fixed in the smart healthcare environment. Then, the accumulated walking frames from the remote patients were transformed into the required binary silhouettes for the sake of noise minimisation and compression purpose. The resulting experimentation shows that the proposed feature extraction approach has significant improvements on the recognition of target gait patterns from the video-based gait analysis of Parkinson’s and normal patients. Accordingly, the proposed VPDIS using feature-weighted minimum distance classifier model provides better prediction time and classification accuracy against the existing healthcare systems that is developed using support vector machine and ensemble learning classifier models.

H-Net: A dual-decoder enhanced FCNN for automated biomedical image diagnosis

Skin lesions and thyroid cancer have become diseases with a high incidence. The computer-aided diagnosis (CAD) system for dermatological diseases offers one of the most remarkable performance where deep learning technologies demonstrate their superiority in surpassing human experts. For developing a CAD system, a critical step is skin lesions and thyroid nodules diagnosis from dermoscopic images and ultrasound images, respectively. Although notable successes have been obtained using deep convolutional neural network (DCNN) models, several challenges hamper the practical applications in clinical due to the complexity of clinical data, e.g., skin lesions or thyroid nodules are irregular shapes or low contrast. To alleviate these issues, we propose a novel dual encoder-decoder network, called H-Net, for automated thyroid nodule and skin lesion segmentation. Specifically, a shallow CNN is applied at its left to learn the low-level details information called L-Net and a deep CNN is employed at its right to capture the high-level information called R-Net. Furthermore, to transfer information between the L-Net and the R-Net mutually, we propose a novel crossed skip connection strategy, which is a specific reliability skip connection way. In addition, to enhance the representation learning ability of the proposed pipeline, we propose a novel contextual information encoding module, which replaces conventional convolutional layers in H-Net. Meanwhile, we propose a novel hybrid loss to alleviate the imbalance training problem. To validate the effectiveness of H-Net, 600 pairs of dermoscopic images and 139 pairs of ultrasound images have been used for evaluation in experiments. Seven latest biomedical image segmentation approaches are compared, and ten metrics are utilized to evaluate the segmentation performance. Extensive experimental results demonstrate that our H-Net yields the new record, achieving a mIoU value of 84.8% on the ISIC-2017 dataset and a mIoU value of 87.5% on the TNUI-2021 dataset, which outperforms state-of-the-art approaches in both visual comparisons and quantitative evaluation. The codes are available at https://github.com/zxg3017/H-Net.

Retraction Note: Hybrid recommendation system for heart disease diagnosis based on multiple kernel learning with adaptive neuro-fuzzy inference system

Retraction Note: Hybrid recommendation system for heart disease diagnosis based on multiple kernel learning with adaptive neuro-fuzzy inference system

Prediction of grape leaf through digital image using FRCNN

A great deal of research indicates that the duality of agricultural production can be reduced because of various factors. Plant diseases are among the most critical aspects of this category. As a result, reducing plant diseases allows for significant improvements in quality. The article uses Grape Leaf Disease Detection Technique (GLDDT) with Faster Region based Convolutional Neural Network (FRCNN) - GLDDT-FRCNN techniques to automatically diagnose plant diseases. Once trained, the software can diagnose plant leaf disease without requiring any experimentation. The primary focus of this research is grape leaf disease. The basic technique manipulates H & colour histograms 25 channels 24. Excluding the final 26 phases, where the person decides which channel (H or a) offers the best separation, the algorithm method is fully automated. A GLDDT is also proposed in this article, which uses two-pronged processes for the evaluation, recognition and categorization of traits. The analysis process, testing on a benchmark set of data reveals that the disease diagnosis system might be a better fit than existing methods because it recognizes and identifies infected/diseased areas. The researchers achieved a precision rate of 99.93% for the detection of Isariopsis, black rot and Esca using the proposed disease detection method.

Wearable gait monitoring for diagnosis of neurodegenerative diseases

Wearable gait monitoring is of significance to improve the quality of clinical diagnosis of neurodegenerative diseases. However, the symptoms of neurodegenerative diseases are always diverse for different subjects. To address this issue, wireless gait monitoring system detecting plantar bend and impact force is proposed. PVDF sensors are arranged as the sensing part. Signal conditioning circuit, WiFi transmission module, and feature signal processing unit are also integrally designed. The proposed system is utilized to 6 healthy adults for normal single walking and cognitive activity interfered walking respectively. Experimental results show that walking interfered by cognitive activities has a smaller plantar bend while larger stride intervals and asymmetry index than normal single walking. Furthermore, the designed system is validated by patients affected with Parkinson’s disease. Results indicate that the proposed method can be implemented as a medical decision support system for diagnosis of neurodegenerative diseases in daily life.

A Proposed Model Expert System for Disease Diagnosis in Children to Make Decisions in First Aid

Children have a weaker immune system than adults. They are susceptible to disease. Therefore, this study proposes an expert system model of disease diagnosis in children. We develop expert strategies to meet the needs of alternative diagnostic tools in making decisions and first aid for children suffering from illness. The development of an expert system model for diagnosing children's diseases using forwarding chaining based on If-Then as an inference engine. We chose the forward chaining method because it has a framework for thinking like a doctor's when diagnosing and concluding the disease. We made testing to model by doctors with 35 patients. The test results show that the expert systems model of disease diagnosis in children in this research has to be used as an alternative or comparison diagnostic tool with an accuracy rate of 79%.

Improved disease diagnosis system for COVID-19 with data refactoring and handling methods.

The novel coronavirus illness (COVID-19) outbreak, which began in a seafood market in Wuhan, Hubei Province, China, in mid-December 2019, has spread to almost all countries, territories, and places throughout the world. And since the fault in diagnosis of a disease causes a psychological impact, this was very much visible in the spread of COVID-19. This research aims to address this issue by providing a better solution for diagnosis of the COVID-19 disease. The paper also addresses a very important issue of having less data for disease prediction models by elaborating on data handling techniques. Thus, special focus has been given on data processing and handling, with an aim to develop an improved machine learning model for diagnosis of COVID-19. Random Forest (RF), Decision tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Support vector machine, and Deep Neural network (DNN) models are developed using the Hospital Israelita Albert Einstein (in São Paulo, Brazil) dataset to diagnose COVID-19. The dataset is pre-processed and distributed DT is applied to rank the features. Data augmentation has been applied to generate datasets for improving classification accuracy. The DNN model dominates overall techniques giving the highest accuracy of 96.99%, recall of 96.98%, and precision of 96.94%, which is better than or comparable to other research work. All the algorithms are implemented in a distributed environment on the Spark platform.

CRT-Net: A generalized and scalable framework for the computer-aided diagnosis of Electrocardiogram signals

Electrocardiogram (ECG) signals play critical roles in the clinical screening and diagnosis of many types of cardiovascular diseases. Despite the fact that deep neural networks have greatly facilitated the computer-aided diagnosis (CAD) in many clinical tasks, the variability and complexity of ECG in practical scenarios still pose significant challenges in diagnostic performance and clinical applications, especially under the current demand of medical cloud computing and remote diagnosis. In this paper, we propose a generalized and scalable framework for the clinical recognition of ECG. Considering the fact that hospitals generally record ECG signals in the form of graphic waves of 2-D images, we first extract the graphic waves of 12-lead images into numerical 1-D ECG signals by a proposed bi-directional connected method. Subsequently, a novel deep neural network, named CRT-Net, is designed to explore waveform features, morphological characteristics, and time-domain features of ECG by embedding convolution neural network (CNN), recurrent neural network (RNN), and transformer module in a scalable deep model, which is especially suitable in clinical scenarios with different lengths of ECG signals captured from different devices. The proposed framework is first evaluated on two widely investigated public repositories, demonstrating superior performance to the state-of-the-art. We also evaluate the effectiveness of the developed framework on clinically collected ECG images from a local hospital. Moreover, based on the proposed algorithms, we have developed a cloud-based ECG system for the computer-aided diagnosis of different cardiovascular diseases.

Sistem Pakar Berbasis Web Diagnosa Penyakit Gigi dan Mulut di Klinik Sehati

The lack of knowledge of dental and oral health and the limited number of dentists causes low public awareness of dental and oral health. This condition makes some people put aside efforts to prevent and treat dental and oral diseases. Therefore, this study has created an expert system for early diagnosis of dental and oral diseases, so that it can help the public to find out about the dental and oral diseases they are suffering from and overcome the problem of scarcity of dental and oral experts. As for designing the decision support system are use the Expert System method for evaluating criteria and sub-criteria, with the Personal Home Page (PHP) programming language, MySQL as a database server, APPSERV as a web server. The results of this information system design are web applications. to input data on the symptoms of the disease suffered by the patient, then carry out the process of selecting the symptoms of the disease and produce a diagnosis result in the form of disease and healing solutions.

An automated 13.5 hour system for scalable diagnosis and acute management guidance for genetic diseases

While many genetic diseases have effective treatments, they frequently progress rapidly to severe morbidity or mortality if those treatments are not implemented immediately. Since front-line physicians frequently lack familiarity with these diseases, timely molecular diagnosis may not improve outcomes. Herein we describe Genome-to-Treatment, an automated, virtual system for genetic disease diagnosis and acute management guidance. Diagnosis is achieved in 13.5 h by expedited whole genome sequencing, with superior analytic performance for structural and copy number variants. An expert panel adjudicated the indications, contraindications, efficacy, and evidence-of-efficacy of 9911 drug, device, dietary, and surgical interventions for 563 severe, childhood, genetic diseases. The 421 (75%) diseases and 1527 (15%) effective interventions retained are integrated with 13 genetic disease information resources and appended to diagnostic reports (https://gtrx.radygenomiclab.com). This system provided correct diagnoses in four retrospectively and two prospectively tested infants. The Genome-to-Treatment system facilitates optimal outcomes in children with rapidly progressive genetic diseases.

A high-performance, hardware-based deep learning system for disease diagnosis.

Modern deep learning schemes have shown human-level performance in the area of medical science. However, the implementation of deep learning algorithms on dedicated hardware remains a challenging task because modern algorithms and neuronal activation functions are generally not hardware-friendly and require a lot of resources. Recently, researchers have come up with some hardware-friendly activation functions that can yield high throughput and high accuracy at the same time. In this context, we propose a hardware-based neural network that can predict the presence of cancer in humans with 98.23% accuracy. This is done by making use of cost-efficient, highly accurate activation functions, Sqish and LogSQNL. Due to its inherently parallel components, the system can classify a given sample in just one clock cycle, i.e., 15.75 nanoseconds. Though this system is dedicated to cancer diagnosis, it can predict the presence of many other diseases such as those of the heart. This is because the system is reconfigurable and can be programmed to classify any sample into one of two classes. The proposed hardware system requires about 983 slice registers, 2,655 slice look-up tables, and only 1.1 kilobits of on-chip memory. The system can predict about 63.5 million cancer samples in a second and can perform about 20 giga-operations per second. The proposed system is about 5–16 times cheaper and at least four times speedier than other dedicated hardware systems using neural networks for classification tasks.

An artifiсial intelligence computer system for differential diagnosis of lysosomal storage diseases

Aim. To improve the efficiency of diagnosis of hereditary lysosomal storage diseases using an intelligent computerbased decision support system.Materials and methods. Descriptions of 35 clinical cases from the literature and depersonalized data of 52 patients from electronic health records were used as material for clinical testing of the computer diagnostic system. Knowledge engineering techniques have been used to extract, structure, and formalize knowledge from texts and experts. Literary sources included online databases and publications (in Russian and English). On this basis, for each clinical form of lysosomal diseases, textological cards were created, the information in which was corrected by experts. Then matrices were formed, including certainty factors (coefficients) for the manifestation, severity, and relevance of signs for each age group (up to 1 year, from 1 to 3 years inclusive, from 4 to 6 years inclusive, 7 years and older). The knowledge base of the expert system was implemented on the ontology network and included a disease model with reference variants of clinical forms. Decision making was carried out using production rules.Results. The expert computer system was developed to support clinical decision-making at the pre-laboratory stage of differential diagnosis of lysosomal storage diseases. The result of its operation was a ranked list of hypotheses, reflecting the degree of their compliance with reference descriptions of clinical disease forms in the knowledge base. Clinical testing was carried out on cases from literary sources and patient data from electronic health records. The criterion for assessing the effectiveness of disease recognition was inclusion of the verified diagnosis in the list of five hypotheses generated by the system. Based on the testing results, the accuracy was 87.4%.Conclusion. The expert system for the diagnosis of hereditary diseases has shown fairly high efficiency at the stage of compiling a differential diagnosis list at the pre-laboratory stage, which allows us to speak about the possibility of its use in clinical practice.

Combination of Problem-Solving and Multi-Organ Ultrasound: A Case Report of AL Amyloidosis Associated with Cardiac, Renal, Peritoneal, Mesenteric and Gastrointestinal Involvement

Introduction: Systemic amyloidosis AL is an unusual disease characterized by a heterogeneous presentation. Clinical Case: We present a case of systemic amyloidosis AL with unusual cardiac, mesenteric, renal, peritoneal, and gastrointestinal involvement, in which the diagnostic hypothesis was formulated on the basis of problem-solving and ultrasound technique (abdomen, lung, heart ultrasound). Conclusion: This case shows that multi-organ ultrasound, associated with problem-solving, may be very helpful in the diagnosis of systemic diseases, even unusual ones, avoiding a long diagnostic interval length.

A Hybrid System for Heart Disease Diagnosis Based on HPCBE Method

Heart disease is a type of chronic disease that can lead to death if not diagnosed in time. A clinical decision support system developed using machine learning technology can be used in the diagnosis of disease. Proper utilization of feature selection increases classification accuracy by reducing the computational cost. The purpose of this research work is to purpose a new Hybrid Pearson Correlation with Backward Elimination (HPCBE) feature selection method. Proposed HPCBE method is further used to develop a hybrid system for heart disease diagnosis (HSHDD).HPCBE is proposed by combining pearson correlation (PC) and backward elimination (BE) methods. Reduced feature subset selected by HPCBE method is used along with decision tree (DT), k-nearest neighbor (KNN), extreme gradient boosting (XGBoost) and adaptive boosting (AdaBoost) classifiers to develop HSHDD. The feature reduction ratio of 53.84% is achieved by the proposed HPCBE Feature Selection method. HSHDD achieved a maximum accuracy of 86.49% in heart disease classification with AdaBoost classifier.

A Novel Fuzzy Parameterized Fuzzy Hypersoft Set and Riesz Summability Approach Based Decision Support System for Diagnosis of Heart Diseases.

Fuzzy parameterized fuzzy hypersoft set (-set) is more flexible and reliable model as it is capable of tackling features such as the assortment of attributes into their relevant subattributes and the determination of vague nature of parameters and their subparametric-valued tuples by employing the concept of fuzzy parameterization and multiargument approximations, respectively. The existing literature on medical diagnosis paid no attention to such features. Riesz Summability (a classical concept of mathematical analysis) is meant to cope with the sequential nature of data. This study aims to integrate these features collectively by using the concepts of fuzzy parameterized fuzzy hypersoft set (-set) and Riesz Summability. After investigating some properties and aggregations of -set, two novel decision-support algorithms are proposed for medical diagnostic decision-making by using the aggregations of -set and Riesz mean technique. These algorithms are then validated using a case study based on real attributes and subattributes of the Cleveland dataset for heart-ailments-based diagnosis. The real values of attributes and subattributes are transformed into fuzzy values by using appropriate transformation criteria. It is proved that both algorithms yield the same and reliable results while considering hypersoft settings. In order to judge flexibility and reliability, the preferential aspects of the proposed study are assessed by its structural comparison with some related pre-developed structures. The proposed approach ensures that reliable results can be obtained by taking a smaller number of evaluating traits and their related subvalues-based tuples for the diagnosis of heart-related ailments.