Intelligent Disease Diagnosis Research Articles

Flexible antibacterial degradable bioelastomer nanocomposites for ultrasensitive human–machine interaction sensing enabled by machine learning

AbstractFlexible wearables have attracted extensive interests for personal human motion sensing, intelligent disease diagnosis, and multifunctional electronic skins. However, the reported flexible sensors, mostly exhibited narrow detection range, low sensitivity, limited degradability to aggravate environmental pollution from vast electronic wastes, and poor antibacterial performance to hardly improve skin discomfort and skin inflammation from bacterial growth under long‐term wearing. Herein, bioinspired from human skin featuring highly sensitive tactile sensation with spinous microstructures for amplifying sensing sensitivity between epidermis and dermis, a wearable antibacterial degradable electronics is prepared from degradable elastomeric substrate with MXene‐coated spinous microstructures templated from lotus leaf assembled with the interdigitated electrode. The degradable elastomer is facilely obtained with tunable modulus to match the modulus of human skin with improved hydrophilicity for rapid degradation. The as‐obtained sensor displays ultra‐low detection limit (0.2 Pa), higher sensitivity (up to 540.2 kPa−1), outstanding cycling stability (>23,000 cycles), a wide detection range, robust degradability, and excellent antibacterial capability. Facilitated by machine learning, the collected sensing signals from the integrated sensors on volunteer's fingers to the related American Sign Language are effectively recognized with an accuracy up to 99%, showing excellent potential in wireless human movement sensing and smart machine learning‐enabled human–machine interaction.

PlantPAD: a platform for large-scale image phenomics analysis of disease in plant science.

Plant disease, a huge burden, can cause yield loss of up to 100%and thus reduce food security. Actually, smart diagnosing diseases with plant phenomics is crucial for recovering the most yield loss, which usually requires sufficient image information. Hence, phenomics is being pursued as an independent discipline to enable the development of high-throughput phenotyping for plant disease. However, we often face challenges in sharing large-scale image data due to incompatibilities in formats and descriptions provided by different communities, limiting multidisciplinary research exploration. To this end, we build a Plant Phenomics Analysis of Disease (PlantPAD) platform with large-scale information on disease. Our platform contains 421 314 images, 63 cropsand 310 diseases. Compared to other databases, PlantPAD has extensive, well-annotated image data and in-depth disease information, and offers pre-trained deep-learning models for accurate plant disease diagnosis. PlantPAD supports various valuable applications across multiple disciplines, including intelligent disease diagnosis, disease educationand efficient disease detection and control. Through three applications of PlantPAD, we show the easy-to-use and convenient functions. PlantPAD is mainly oriented towards biologists, computer scientists, plant pathologists, farm managersand pesticide scientists, which may easily explore multidisciplinary research to fight against plant diseases. PlantPAD is freely available at http://plantpad.samlab.cn.

Multi-level spatial-temporal and attentional information deep fusion network for retinal vessel segmentation

Objective. Automatic segmentation of fundus vessels has the potential to enhance the judgment ability of intelligent disease diagnosis systems. Even though various methods have been proposed, it is still a demanding task to accurately segment the fundus vessels. The purpose of our study is to develop a robust and effective method to segment the vessels in human color retinal fundus images. Approach. We present a novel multi-level spatial-temporal and attentional information deep fusion network for the segmentation of retinal vessels, called MSAFNet, which enhances segmentation performance and robustness. Our method utilizes the multi-level spatial-temporal encoding module to obtain spatial-temporal information and the Self-Attention module to capture feature correlations in different levels of our network. Based on the encoder and decoder structure, we combine these features to get the final segmentation results. Main results. Through abundant experiments on four public datasets, our method achieves preferable performance compared with other SOTA retinal vessel segmentation methods. Our Accuracy and Area Under Curve achieve the highest scores of 96.96%, 96.57%, 96.48% and 98.78%, 98.54%, 98.27% on DRIVE, CHASE_DB1, and HRF datasets. Our Specificity achieves the highest score of 98.58% and 99.08% on DRIVE and STARE datasets. Significance. The experimental results demonstrate that our method has strong learning and representation capabilities and can accurately detect retinal blood vessels, thereby serving as a potential tool for assisting in diagnosis.

Metadata and Image Features Co-Aware Personalized Federated Learning for Smart Healthcare.

Recently, artificial intelligence has been widely used in intelligent disease diagnosis and has achieved great success. However, most of the works mainly rely on the extraction of image features but ignore the use of clinical text information of patients, which may limit the diagnosis accuracy fundamentally. In this paper, we propose a metadata and image features co-aware personalized federated learning scheme for smart healthcare. Specifically, we construct an intelligent diagnosis model, by which users can obtain fast and accurate diagnosis services. Meanwhile, a personalized federated learning scheme is designed to utilize the knowledge learned from other edge nodes with larger contributions and customize high-quality personalized classification models for each edge node. Subsequently, a Naïve Bayes classifier is devised for classifying patient metadata. And then the image and metadata diagnosis results are jointly aggregated by different weights to improve the accuracy of intelligent diagnosis. Finally, the simulation results illustrate that, compared with the existing methods, our proposed algorithm achieves better classification accuracy, reaching about 97.16% on PAD-UFES-20 dataset.

Multichannel Flexible Pulse Perception Array for Intelligent Disease Diagnosis System.

Pressure sensors with high sensitivity, a wide linear range, and a quick response time are critical for building an intelligent disease diagnosis system that directly detects and recognizes pulse signals for medical and health applications. However, conventional pressure sensors have limited sensitivity and nonideal response ranges. We proposed a multichannel flexible pulse perception array based on polyimide/multiwalled carbon nanotube-polydimethylsiloxane nanocomposite/polyimide (PI/MPN/PI) sandwich-structure pressure sensor that can be applied for remote disease diagnosis. Furthermore, we established a mechanical model at the molecular level and guided the preparation of MPN. At the structural level, we achieved high sensitivity (35.02 kPa-1) and a broad response range (0-18 kPa) based on a pyramid-like bilayer microstructure with different upper and lower surfaces. A 27-channel (3 × 9) high-density sensor array was integrated at the device level, which can extract the spatial and temporal distribution information on a pulse. Furthermore, two intelligent algorithms were developed for extracting six-dimensional pulse information and automatic pulse recognition (the recognition rate reaches 97.8%). The results indicate that intelligent disease diagnosis systems have great potential applications in wearable healthcare devices.

A hybrid cost-sensitive machine learning approach for the classification of intelligent disease diagnosis

Automatic risk classification of diseases is one of the most significant health problems in medical and healthcare domain. However, the related studies are relative scarce. In this paper, we design an intelligent diagnosis model based on optimal machine learning algorithms with rich clinical data. First, the disease risk classification problem based on machine learning is defined. Then, the K-means clustering algorithm is used to validate the class label of given data, thereby removing misclassified instances from the original dataset. Furthermore, naive Bayesian algorithm is applied to build the final classifier by using 10-fold cross-validation method. In addition, a novel class-specific attribute weighted approach is adopted to alleviate the conditional independence assumption of naive Bayes, which means we assign each disease attribute a specific weight for each class. Last but not least, a hybrid cost-sensitive disease risk classification model is formulated, and a practical example from the University of California Irvine (UCI) machine learning database is used to illustrate the potential of the proposed method. Experimental results demonstrate that the approach is competitive with the state-of-the-art classifiers.

Salp swarm optimisation with deep transfer learning enabled retinal fundus image classification model

Automated screening and diagnostic process in the healthcare sector improves services, reduces cost and labour. With the developments of machine learning (ML) and deep learning (DL) models, intelligent disease diagnosis models can be designed. Retinal fundus image classification using DL models becomes essential for the identification and classification of distinct retinal diseases. This article develops a salp swarm optimisation with deep transfer learning enabled retinal fundus image classification (SSODTL-RFIC) model. The proposed SSODTL-RFIC model examines the retinal fundus image for the existence of diseases. In addition, a median filtering (MF) approach is employed for the noise removal process and graph cut (GC) segmentation is applied. Besides, MobileNetv1 feature extractor is involved to produce feature vectors. Finally, SSO with cascade forward neural network (CFNN) model is applied for recognition and classification process. A widespread experimentation process is performed on benchmark datasets to examine the enhanced performance of the SSODTL-RFIC model, an extensive comparative examination pointed out the supremacy of the SSODTL-RFIC model over the recent approaches with maximum accuracy of 98.71% and 99.12% on the test ARIA and STARE datasets respectively.

Intelligent Disease Diagnosis Model for Energy Aware Cluster Based IoT Healthcare Systems

In recent days, advancements in the Internet of Things (IoT) and cloud computing (CC) technologies have emerged in different application areas, particularly healthcare. The use of IoT devices in healthcare sector often generates large amount of data and also spent maximum energy for data transmission to the cloud server. Therefore, energy efficient clustering mechanism is needed to effectively reduce the energy consumption of IoT devices. At the same time, the advent of deep learning (DL) models helps to analyze the healthcare data in the cloud server for decision making. With this motivation, this paper presents an intelligent disease diagnosis model for energy aware cluster based IoT healthcare systems, called IDDM-EAC technique. The proposed IDDM-EAC technique involves a 3-stage process namely data acquisition, clustering, and disease diagnosis. In addition, the IDDM-EAC technique derives a chicken swarm optimization based energy aware clustering (CSOEAC) technique to group the IoT devices into clusters and select cluster heads (CHs). Moreover, a new coyote optimization algorithm (COA) with deep belief network (DBN), called COA-DBN technique is employed for the disease diagnostic process. The COA-DBN technique involves the design of hyperparameter optimizer using COA to optimally adjust the parameters involved in the DBN model. In order to inspect the betterment of the IDDM-EAC technique, a wide range of experiments were carried out using real time data from IoT devices and benchmark data from UCI repository. The experimental results demonstrate the promising performance with the minimal total energy consumption of 63% whereas the EEPSOC, ABC, GWO, and ACO algorithms have showcased a higher total energy consumption of 69%, 78%, 83%, and 84% correspondingly.

Magnetic-Nanowaxberry-Based Simultaneous Detection of Exosome and Exosomal Proteins for the Intelligent Diagnosis of Cancer.

Exosome concentration and exosomal proteins are regarded as promising cancer biomarkers. Herein, a waxberry-like magnetic bead (magnetic-nanowaxberry) which has huge surface area and strong affinity was synthesized to couple with aptamer for exosome capture and recovery. Subsequently, we developed a fluorescent assay for the sensitive, accurate, and simultaneous quantification of exosome and cancer-related exosomal proteins [epidermal growth factor receptor (EGFR) and epithelial cell adhesion molecule (EpCAM)] by using triple-colored probes to recognize EGFR and EpCAM or spontaneously anchor to the lipid bilayer. In this design, the interference of soluble proteins can be avoided due to the dual recognition strategy. Moreover, the lipid-based quantification of exosome concentration can improve the accuracy. Besides, the simultaneous detection mode can save samples and simplify the operation steps. Consequently, the assay shows high sensitivity (the limits of detection are down to 0.96 pg/mL for EGFR, 0.19 pg/mL for EpCAM, and 2.4 × 104 particles/μL for exosome), high specificity, and satisfactory accuracy. More importantly, this technique is successfully used to analyze exosomes in plasma to distinguish cancer patients from healthy individuals. To improve the diagnostic efficacy, the deep learning was used to exploit the potential pattern hidden in data obtained by the proposed method. Also, the accuracy for the intelligent diagnosis of cancer can achieve 96.0%. This study provides a new avenue for developing new biosensors for exosome analysis and intelligent disease diagnosis.

Optimal deep learning approaches and healthcare big data analytics for mobile networks toward 5G

Recent developments in wireless networking, big data technologies like 5G networks, healthcare big data analytics, Internet of Things (IoT) and other advanced technologies in wearables and Artificial Intelligence (AI) have enabled the development of intelligent disease diagnosis models. The current study devises a new big data analytic-based feature selection and Deep Belief Network (DBN)-based disease diagnostic model. To reduce the number of features and curse of dimensionality, a Link-based Quasi Oppositional Binary Particle Swarm Optimization Algorithm is used in feature selection to narrow down an optimal set of features. The application of quasi-oppositional mechanism in BPSO algorithm helps in increasing the convergence rate. Followed by, the DBN model is applied as a classifier to diagnose the existence of disease from feature-reduced data. A series of experiments was conducted to emphasize the performance of the presented model. The obtained experimental outcomes showcased that the presented model yielded better results in several aspects.

IoT enabled depthwise separable convolution neural network with deep support vector machine for COVID-19 diagnosis and classification.

At present times, the drastic advancements in the 5G cellular and internet of things (IoT) technologies find useful in different applications of the healthcare sector. At the same time, COVID-19 is commonly spread from animals to persons, but today it is transmitting among persons by adapting the structure. It is a severe virus and inappropriately resulted in a global pandemic. Radiologists utilize X-ray or computed tomography (CT) images to diagnose COVID-19 disease. It is essential to identify and classify the disease through the use of image processing techniques. So, a new intelligent disease diagnosis model is in need to identify the COVID-19. In this view, this paper presents a novel IoT enabled Depthwise separable convolution neural network (DWS-CNN) with Deep support vector machine (DSVM) for COVID-19 diagnosis and classification. The proposed DWS-CNN model aims to detect both binary and multiple classes of COVID-19 by incorporating a set of processes namely data acquisition, Gaussian filtering (GF) based preprocessing, feature extraction, and classification. Initially, patient data will be collected in the data acquisition stage using IoT devices and sent to the cloud server. Besides, the GF technique is applied to remove the existence of noise that exists in the image. Then, the DWS-CNN model is employed for replacing default convolution for automatic feature extraction. Finally, the DSVM model is applied to determine the binary and multiple class labels of COVID-19. The diagnostic outcome of the DWS-CNN model is tested against Chest X-ray (CXR) image dataset and the results are investigated interms of distinct performance measures. The experimental results ensured the superior results of the DWS-CNN model by attaining maximum classification performance with the accuracy of 98.54% and 99.06% on binary and multiclass respectively.

Application and Development of Artificial Intelligence and Intelligent Disease Diagnosis.

With the continuous development of artificial intelligence (AI) technology, big data-supported AI technology with considerable computer and learning capacity has been applied in diagnosing different types of diseases. This study reviews the application of expert systems, neural networks, and deep learning used by AI technology in disease diagnosis. This paper also gives a glimpse of the intelligent diagnosis and treatment of digestive system diseases, respiratory system diseases, and osteoporosis by AI technology.

Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization.

With the widespread adoption of e-Healthcare and telemedicine applications, accurate, intelligent disease diagnosis systems have been profoundly coveted. In recent years, numerous individual machine learning-based classifiers have been proposed and tested, and the fact that a single classifier cannot effectively classify and diagnose all diseases has been almost accorded with. This has seen a number of recent research attempts to arrive at a consensus using ensemble classification techniques. In this paper, a hybrid system is proposed to diagnose ailments using optimizing individual classifier parameters for two classifier techniques, namely, support vector machine (SVM) and multilayer perceptron (MLP) technique. We employ three recent evolutionary algorithms to optimize the parameters of the classifiers above, leading to six alternative hybrid disease diagnosis systems, also referred to as hybrid intelligent systems (HISs). Multiple objectives, namely, prediction accuracy, sensitivity, and specificity, have been considered to assess the efficacy of the proposed hybrid systems with existing ones. The proposed model is evaluated on 11 benchmark datasets, and the obtained results demonstrate that our proposed hybrid diagnosis systems perform better in terms of disease prediction accuracy, sensitivity, and specificity. Pertinent statistical tests were carried out to substantiate the efficacy of the obtained results.

Design and development of web enabled fuzzy expert system using rule advancement strategy

This paper reports a new approach for providing intelligence in the expert system for diagnosis of diseases for rose flower. It describes the development of a web-based intelligent disease diagnosis system. This expert system is based on a fuzzy logic approach and the Euclidean distance method. This approach is based on rule advancement strategy. This approach enables the drawing of inferences with the enhanced intelligence. This approach is used in the existing fuzzy technique for inferencing in expert system. The proposed expert system incorporates new features: 1) development of knowledge management platform; 2) dynamic knowledge base creation strategy. The dynamically prompted rules are derived from those diagnosis sessions which resulted in successful decisions. This enables more efficient decision-making in the future sessions; 3) dynamic knowledge acquisition; 4) explanation facility which incorporates the rule firing history and rule explanation generator. This expert system gives an acceptable diagnosis of diseases. The inferences are drawn faster compared to traditional approaches. The proposed expert system which is based on rule advancement strategy has been tested for flower rose.

Using Fuzzy Concept Lattice for Intelligent Disease Diagnosis

This paper proposes a novel intelligent disease diagnosis method based on fuzzy concept lattice. Symptoms and the corresponding extents (e.g., frequency, severity, and duration) of each disease can be extracted to form a fuzzy concept lattice. The fuzzy concept lattice of the symptoms and their extents to be diagnosed needs to be constructed to match the fuzzy concept lattice of possible diseases. The similarity between the above two types of concept lattices can be calculated and used to aid for effective diagnosis. Naturally, the disease with the largest similarity is the finding of intelligent diagnosis. In the future, more efficient fuzzy concept lattice construction method and update algorithm will be explored, which are presumed to be very complicated.

An Intelligent Disease Diagnosis System by Fuzzy Similarity Distance

The progress of Artificial intelligence and Information technology has been driving research of intelligent diagnosing, and the efficiency of diagnosis is improved by disease diagnosing system .In this paper, we analyze the comparability and relativity between fuzzy similarity distance and disease diagnosis, and initiate the theoretical model of fuzzy-distance on the basis of fuzzy membership factors vectors, and some corresponding data structure. In addition, the inference algorithm and the hierarchy of system are brought forward. Experimentation statistics demonstrate that the novel diagnosis system could obtain a satisfying accuracy rate of diagnosis, and low a rate of misdiagnosis effectively.

An intelligent multimedia interface for fuzzy-logic based inference in crops

The paper reports an interface designed for three subsystems—the knowledge acquisition subsystem with dynamic disease knowledge base, intelligent disease diagnosis subsystem with object-oriented intelligent-inference model and intelligent tutor for crop disease with audio-visual graphical user web-interface. The paper describes the design features; functionality and development of intelligent multimedia web interface. The results of its evaluation are also presented. A novel approach of rule promotion based on fuzzy logic is used in the system for drawing intelligent inferences for crop disease management. A text-to-speech (TTS) converter is used for providing capability of text-to-talking user interface. It provides the highly-effective interactive user interface on web for live interactions. It helps significantly and gives solutions of plant pathological problems in short spell. The application software is built using the Microsoft .NET framework, ASP.NET web-application framework provided in Visual Studio.NET. The Microsoft Speech SDK (5.1) is used to develop text to voice multimedia interface. The dynamic knowledge base is implemented using SQL server.

A web-based intelligent disease-diagnosis system using a new fuzzy-logic based approach for drawing the inferences in crops

This paper suggests a new approach for providing intelligence in the system for diagnosis of diseases of the oilseed-crops. It reports the development of a web-based intelligent disease diagnosis system (WIDDS). The WIDDS is based on a new fuzzy logic approach. The approach is based on use of a rule-promotion methodology. This approach enables the drawing of inferences with the enhanced intelligence. The WIDDS also incorporates new features that improve the presently existing expert systems. The new features are (i) object-oriented (O-O) inference model, (ii) dynamic knowledge base creation strategy. The dynamically promoted rules are derived from those diagnosis sessions, which resulted in successful decisions. This enables more efficient decision-making in the future sessions, (iii) audio–visual–graphical user interface using text-to-speech (TTS) conversion tools. The WIDDS results in decreasing not only the number of interactive question–answer sessions with the clients but also leads to acceptable diagnosis. Further, the inferences are drawn faster compared to the traditional approach, which is the expert based reasoning method. The suggested WIDDS, which is based on rule-promotion approach, has been tested for three oilseeds crops – soybean, groundnut and rapeseed-mustard.

Integrated design of the intelligent web-based Chinese Medical Diagnostic System (CMDS) – Systematic development for digestive health

Abstract Chinese Medicine (CM) has been recognized as the popular complementary medicine in the world and the abundance of healing knowledge has been transmitted and enriched from generation to generation. The well-recorded archive of Chinese medical knowledge has grown and developed into very detailed theory and philosophy together with a comprehensive and subtle system of diagnosis. Recently, the Internet has been quite often used in a variety of applications for medical purpose, i.e. to provide real-time and platform-independent access which can build and share knowledge about clinical diagnosis experiences, knacks, diseases treatments, and even prescriptions. In this respect, there is a real need of an intelligent system for Chinese medical physician or education because Chinese medicine has evolved complex methods of diagnosis and treatment tailored to the individual’s subtle patterns of disharmony. This allows it not only to treat fully manifest diseases, but also to assist in maintaining health and balance to prevent illnesses from occurring. Ontologies become an important mechanism to build knowledge-based systems. The role of ontologies is to capture domain knowledge and provide a commonly agreed upon understanding of a domain. The advantages include the sharing and re-use of knowledge, and the better engineering of knowledge-based system with respect to acquisition, verification, and maintenance. In this paper, we propose a unifying framework for intelligent disease diagnosis system named CMDS – Chinese Medical Diagnostic System where the medical ontology has been integrated for the system development, and the methodologies of its implementation for digestive health. CMDS uses web interface and expert system technology to act as human expertise and diagnose a number of digestive system diseases. Besides, to efficiently elicit the knowledge of digestive system from domain experts and construct the medical ontology, a hybrid knowledge acquisition strategy is proposed. CMDS provides a truly precise analysis for digestive system disease and the prototype system can diagnose up to 50 types of diseases amongst 10 species of primary digestive system, and just uses over 500 rules and 600 images for various diseases. The satisfactory performance of the system has proven that it could serve the educational purpose, act as a consultant role and the functionality can be extended to the whole body health system.