Medical Diagnostic Support System Research Articles

Medical diagnostic support system based on breast thermography using Raspberry Pi and cloud computing

Breast thermography is a promising medical imaging technique for the detection of breast cancer. However, providing a robust and portable computer-aided diagnostic system for breast thermography remains a tedious task. In this paper, a computer-aided diagnostic system based on breast thermography is developed and implemented on a Raspberry Pi 4 using the cloud computing services to provide the computing power needed for machine learning algorithms. Image processing techniques such as pre-processing and segmentation are employed to achieve an adequate feature extraction task. The Support Vector Machine classifier is used in the final stage to classify the breast as normal or abnormal. According to the experimental results, the proposed computer-aided diagnostic system has shown high performance in both the segmentation and classification steps. Furthermore, a low computation time was obtained when using the high computing capabilities of the cloud with the Raspberry Pi. We conclude that the implementation of such a decision support system on the Raspberry Pi especially when using the cloud computing services, can be a reliable tool for radiologists to predict breast abnormalities even in the rural backcountry where there is lack of health services.

Images indexing and matched assessment of semantics and visuals similarities applied to a medical learning X-ray image base

Medical diagnostic support systems are important tools in the field of radiology. However, the precision obtained, during the exploitation of high homogeneity image datasets, needs to be improved. To develop a new learning system dedicated to public health practitioners. This study presents an upgraded version dedicated to radiology experts for better clinical decision-making when diagnosing and treating the patient (CAD approach). Our system is a hybrid approach based on a matching of semantic and visual attributes of images. It is a combination of two complementary subsystems to form the intermodal system. The first one named α based on semantic attributes. Indexing and image retrieval based on specific keywords. The second system named β based on low-level attributes. Vectors characterizing the digital content of the image (color, texture and shape) represent images. Our image database consists of 930 X-ray images including 320 mammograms acquired from the mini-MIAS database of mammograms and 610 X-rays acquired from the Public Hospital Establishment (EPH-Rouiba Algeria). The combination of two subsystems gives rise to the intermodal system: α-subsystem offers an overall result (based on visual descriptors), then β-subsystem (low level descriptors) refines the result and increases relevance. Our system can perform a specific image search (in a database of images with very high homogeneity) with an accuracy of around 90% for a recall of 25%. The average (overall) accuracy of the system exceeds 70%. The results obtained are very encouraging, and demonstrate efficiency of our approach, particularly for the intermodal system.

Usefulness of a medical interview support application for residents: A pilot study.

To conduct an appropriate medical interview, education and clinical experience are necessary. The usefulness of computer-based medical diagnostic support systems has been reported in medical interviewing. However, only a few reports have actually applied these systems and noted changes in the quality of the medical interview of residents. We aimed to examine how the use of a medical interview support application changes the medical interviews of residents. The study was conducted on 15 residents (with less than two years post-graduation) and ran from November 2020 to March 2021. Faculty members played the role of simulated patients in 20 cases, and the residents conducted the medical interviews. In 10 of the 20 cases, a medical interview support application was used. After the interview, the residents were asked to list up to 10 differential diseases; the interview was considered appropriate if it included the disease portrayed by the simulated patient. Furthermore, the duration of the medical interview, the number of questions asked, and changes in stress parameters were evaluated. The use of a medical interview support application increased the percentage of appropriate medical interviews. Considering the frequency, the use of a medical interview support application increased the rate of appropriate medical interviews in the rare disease group, as well as the number of questions and duration of the interviews. No stress reduction was observed. The medical interview support application may be a useful tool in identifying appropriate differential diseases during medical interviews by residents.

Levenberg-Marquardt Neural Network Algorithm for Degree of Arteriovenous Fistula Stenosis Classification Using a Dual Optical Photoplethysmography Sensor.

This paper proposes a noninvasive dual optical photoplethysmography (PPG) sensor to classify the degree of arteriovenous fistula (AVF) stenosis in hemodialysis (HD) patients. Dual PPG measurement node (DPMN) becomes the primary tool in this work for detecting abnormal narrowing vessel simultaneously in multi-beds monitoring patients. The mean and variance of Rising Slope (RS) and Falling Slope (FS) values between before and after HD treatment was used as the major features to classify AVF stenosis. Multilayer perceptron neural networks (MLPN) training algorithms are implemented for this analysis, which are the Levenberg-Marquardt, Scaled Conjugate Gradient, and Resilient Back-propagation, to identify the degree of HD patient stenosis. Eleven patients were recruited with mean age of 77 ± 10.8 years for analysis. The experimental results indicated that the variance of RS in the HD hand between before and after treatment was significant difference statistically to stenosis (p < 0.05). Levenberg-Marquardt algorithm (LMA) was significantly outperforms the other training algorithm. The classification accuracy and precision reached 94.82% and 92.22% respectively, thus this technique has a potential contribution to the early identification of stenosis for a medical diagnostic support system.

Medical diagnosis with the aid of using fuzzy logic and intuitionistic fuzzy logic

The objective of the present study is to develop/establish a web-based medical diagnostic support system (MDSS) by which health care support can be provided for people living in rural areas of a country. In this respect, this research provides a novel approach for medical diagnosis driven by integrating fuzzy and intuitionistic fuzzy (IF) frameworks. Subsequently, based on the proposed approach a web-based MDSS is developed. The proposed MDSS comprises of a knowledge base (KB) and intuitionistic fuzzy inference system (IFIS). Based on the observation that medical data cannot be described with both precision and certainty, a medical KB is constructed in the form of a set of if-then decision rules by employing both fuzzy and IF logics. After constructing the medical KB, a new set of patients is considered for diagnosing the diseases. For each patient, linguistic values of the patients' symptoms are considered as inputs of the proposed IFIS and modeled by using the generalized triangular membership functions. Subsequently, integrated fuzzy and IF rule-based inference system is used to find a valid conclusion for the new set of patients. In a nutshell, in this paper fuzzy rule-based and IFS based inference systems are combined for better and more realistic representation of uncertainty of the medical diagnosis problem and for more accurate diagnostic result. The method is composed of following four steps: (1) the modeling of antecedent part of the rules, which consist of linguistic assessments of the patients' symptoms provided by the doctors/medical experts with their corresponding confidence levels, by using generalized fuzzy numbers; (2) the modeling of consequent part, which reveals the degree of association and the degree of non-association of diseases into the patient, by using IFSs; (3) the use of IF aggregation operator in inference process; (4) the application of relative closeness function to find the final crisp output for a given diagnosis. Finally, the applicability of the proposed approach is illustrated with a suitable case study. This article has also justified the proposed approach by using similarity measurement.

Feature selection for medical diagnosis : Evaluation for cardiovascular diseases

Machine learning has emerged as an effective medical diagnostic support system. In a medical diagnosis problem, a set of features that are representative of all the variations of the disease are necessary. The objective of our work is to predict more accurately the presence of cardiovascular disease with reduced number of attributes. We investigate intelligent system to generate feature subset with improvement in diagnostic performance. Features ranked with distance measure are searched through forward inclusion, forward selection and backward elimination search techniques to find subset that gives improved classification result. We propose hybrid forward selection technique for cardiovascular disease diagnosis. Our experiment demonstrates that this approach finds smaller subsets and increases the accuracy of diagnosis compared to forward inclusion and back-elimination techniques.

Foreword of Special Issue on “Nomadic Services and Applications”

Foreword of Special Issue on “Nomadic Services and Applications”

Interactive E-diagnosis: An Efficient Scheme for Medical Diagnosis Support System

With advancements in information technology, computer applications have now been largely implemented in health care, where medical support system or expert system is serving as a second opinion for medical personnel, underscoring its importance. The present study aims to design a prototype medical diagnostic support system, in which DICOM-based image analysis algorithms are utilized to develop an image browser and graphical user interface (GUI), allowing medical personnel to read X-ray, CT scan, MRI or other medical imaging files via a simple browser interface. Practitioners may also configure parameters for the intensity slicing and intensity analysis based on their medical expertise and experience to automatically generate converted imaging results for use as diagnostic references. Furthermore, the integration of GUI and network programming technology facilitates patient consultation and discussions among doctors in different areas, making this medical diagnosis system a telemedicine application, as well as an e-learning tool.

The Medical Diagnostic Support System using Extended Neural Network Based on Intelligent Multiagent Techniques

The Medical Diagnostic Support System using Extended Neural Network Based on Intelligent Multiagent Techniques

The medical diagnostic support system using extended Rough Neural Network and Multiagent

Multiagent technologies enable us to explore their sociological and psychological foundations. Amedical dignostic support system is built using this. Moreover, We think that the data inputted can acquire higher diagnostic accuracy by sorting out using a determination table. In this paper, the recurrence diagnostic system of cancer is built and the output error of Multiagents learning method into the usual Neural Network and a Rough Neural Network and Genetic Programming be compared. The data of the prostates cancer offered by the medical institution and a renal cancer was used for verification of a system.

Improving diagnostic accuracy using a hierarchical neural network to model decision subtasks

A number of quantitative models including linear discriminant analysis, logistic regression, k nearest neighbor, kernel density, recursive partitioning, and neural networks are being used in medical diagnostic support systems to assist human decision-makers in disease diagnosis. This research investigates the decision accuracy of neural network models for the differential diagnosis of six erythamatous-squamous diseases. Conditions where a hierarchical neural network model can increase diagnostic accuracy by partitioning the decision domain into subtasks that are easier to learn are specifically addressed. Self-organizing maps (SOM) are used to portray the 34 feature variables in a two dimensional plot that maintains topological ordering. The SOM identifies five inconsistent cases that are likely sources of error for the quantitative decision models; the lower bound for the diagnostic decision error based on five errors is 0.0140. The traditional application of the quantitative models cited above results in diagnostic error levels substantially greater than this target level. A two-stage hierarchical neural network is designed by combining a multilayer perceptron first stage and a mixture-of-experts second stage. The second stage mixture-of-experts neural network learns a subtask of the diagnostic decision, the discrimination between seborrheic dermatitis and pityriasis rosea. The diagnostic accuracy of the two stage neural network approaches the target performance established from the SOM with an error rate of 0.0159.

A database medical diagnostic support system using standardized medical data: a pilot study

In the past two decades a wide variety of information systems have been developed to facilitate medical diagnosis, better known as medical expert systems. These systems can be divided into several methodological categories, one of which is the database approach. A problem with this approach is the difficulty in filling the knowledge base directly from the source text because of complexities of natural language. This paper discusses the development of a computer-aided medical di agnostic system using standardized symptom codes as its input. based upon the results of a comprehensive questionnaire that is filled out by the patient. Subsequently, the patient's potential disease is predicted by linking symptoms with most likely diseases. The methodology of the system is discussed and a few examples are presented, as well as directions for further devel opment of this approach to medical information processing and decision making.

第11回日本自動化健診学会学術大会講演抄録 臨床検査データに基づく診断サポートシステム

第11回日本自動化健診学会学術大会講演抄録 臨床検査データに基づく診断サポートシステム