Classification In Medicine Research Articles

Gray-Scale Extraction of Bone Features from Chest Radiographs Based on Deep Learning Technique for Personal Identification and Classification in Forensic Medicine.

Post-mortem (PM) imaging has potential for identifying individuals by comparing ante-mortem (AM) and PM images. Radiographic images of bones contain significant information for personal identification. However, PM images are affected by soft tissue decomposition; therefore, it is desirable to extract only images of bones that change little over time. This study evaluated the effectiveness of U-Net for bone image extraction from two-dimensional (2D) X-ray images. Two types of pseudo 2D X-ray images were created from the PM computed tomography (CT) volumetric data using ray-summation processing for training U-Net. One was a projection of all body tissues, and the other was a projection of only bones. The performance of the U-Net for bone extraction was evaluated using Intersection over Union, Dice coefficient, and the area under the receiver operating characteristic curve. Additionally, AM chest radiographs were used to evaluate its performance with real 2D images. Our results indicated that bones could be extracted visually and accurately from both AM and PM images using U-Net. The extracted bone images could provide useful information for personal identification in forensic pathology.

Automated Method for Intracranial Aneurysm Classification Using Deep Learning.

Intracranial aneurysm (IA) is now a common term closely associated with subarachnoid hemorrhage. IA is the bulging of a blood vessel caused by a weakening of its wall. This bulge can rupture and, in most cases, cause internal bleeding. In most cases, internal bleeding leads to death or other fatal consequences. Therefore, the development of an automated system for detecting IA is needed to help physicians make more accurate diagnoses. For this reason, we have focused on this problem. In this paper, we propose a 2D Convolutional Neural Network (CNN) based on a network commonly used for data classification in medicine. In addition to our proposed network, we also tested ResNet 50, ResNet 101 and ResNet 152 on a publicly available dataset. In this case, ResNet 152 achieved better results than our proposed network, but our network was significantly smaller and the classifications took significantly less time. Our proposed network achieved an overall accuracy of 98%. This result was achieved on a dataset consisting of 611 images. In addition to the mentioned networks, we also experimented with the VGG network, but it was not suitable for this type of data and achieved only 20%. We compare the results in this work with neural networks that have been verified by the scientific community, and we believe that the results obtained by us can help in the creation of an automated system for the detection of IA.

Modelling methods of artificial intelligence in medical application

Artificial intelligence is a branch of computer science, an intelligent system that can simulate human thinking, recognize complex situations, acquire learning abilities and knowledge, and solve problems. With the continuous development of information technology, artificial intelligence techniques are increasingly being improved and applied to large-scale genetics research, image detection and classification in medicine. Predictive models for medical data can be built using a wide range of machine learning algorithms: decision trees, multilayer perceptrons, plain Bayes, random forests, and support vector machines, etc., thus processing massive, high-dimensional data and conducting medical research. This paper addresses the specific applications of artificial intelligence in medical practice.

A Review on the Classification of Traditional Chinese Medicine Tongue Images Based on Computer Technology

Tongue imaging in traditional Chinese medicine is one of the important indicators for diagnosis, and its research has become a hot topic in the field of traditional Chinese medicine. In recent years, with the continuous development of computer technology, more and more research has applied computer technology to the analysis and diagnosis of tongue image classification in traditional Chinese medicine, which is of great significance for the diagnosis and treatment of traditional Chinese medicine. The research on the classification of tongue images is also of great importance. This article reviews the current status and existing problems of tongue image research.

Infrasound- implications for human medicine

Infrasound describes ubiquitous, low-frequency sound (< 20 Hz) in the environment with along wavelength below the median hearing threshold, which can nevertheless be heard and tactilely perceived, depending on the sound pressure level and frequency spectrum. In nature, infrasound emissions usually occur only in the low-threshold range. Nevertheless, after strong and chronic exposure to usually artificially generated infrasound emissions, various effects on the ear and the body, sometimes questionably critical to health, can be observed. Correct measurement and assessment of infrasound sources is complex and controversial. Established guidelines are scarce. Innovative research areas include infrasound monitoring for evaluation of natural events and infrasound applications in medicine. In the future, it is hoped that new insights will be gained from infrasound research and that amore extensive classification in occupational medicine will be possible.

An introduction to machine learning for classification and prediction.

Classification and prediction tasks are common in health research. With the increasing availability of vast health data repositories (e.g. electronic medical record databases) and advances in computing power, traditional statistical approaches are being augmented or replaced with machine learning (ML) approaches to classify and predict health outcomes. ML describes the automated process of identifying ("learning") patterns in data to perform tasks. Developing an ML model includes selecting between many ML models (e.g. decision trees, support vector machines, neural networks); model specifications such as hyperparameter tuning; and evaluation of model performance. This process is conducted repeatedly to find the model and corresponding specifications that optimize some measure of model performance. ML models can make more accurate classifications and predictions than their statistical counterparts and confer greater flexibility when modelling unstructured data or interactions between covariates; however, many ML models require larger sample sizes to achieve good classification or predictive performance and have been criticized as "black box" for their poor transparency and interpretability. ML holds potential in family medicine for risk profiling of patients' disease risk and clinical decision support to present additional information at times of uncertainty or high demand. In the future, ML approaches are positioned to become commonplace in family medicine. As such, it is important to understand the objectives that can be addressed using ML approaches and the associated techniques and limitations. This article provides a brief introduction into the use of ML approaches for classification and prediction tasks in family medicine.

Chromosome Mediated Fluoroquinolone and Extended Spectrum Beta-lactamase Resistant Genes in E. coli of Poultry Origin in Ekiti State

Background: One health approach aimed at solving global health crisis links human, animal, and environmental health. This inclusive strategy has contributed to antibiotic classification in both human and animal medicine.&#x0D; Aims: The aims of this research work are to determine the phylogenetic relationship of E. coli isolated from poultry and waste sources. The presence of chromosome mediated fluoroquinolone and extended spectrum beta-lactamase resistant genes will also be detected in the isolates.&#x0D; Study Design: Experimental design.&#x0D; Methodology: Data on farming attitudes of poultry farmers were collected using a questionnaire. E. coli was isolated from fresh poultry droppings and waste disposal sites using eosine methylene blue agar. The antibiotic sensitivity profile of the isolates was determined using the modified Kirby Bauer disc diffusion method. Phenotypic expression of fluoroquinolone (qnrS) and beta-lactamase (blaCMY) resistant traits were further detected using Polymerase Chain Reaction. The 16S rRNA gene sequencing was carried out followed by sequence alignment of E. coli genes with those from GenBank sources to determine the molecular identity of the isolates. Spearman’s correlation coefficient (rs) was run to determine the relationship between antibiotic treatment and resistant profile of the isolates. The phylogenetic relationship of the isolates was determined using Bio edit and Mega 6 software.&#x0D; Results: Organic poultry farming was practiced by small-scaled, peasant farmers who raised free range birds while antibiotics were widely used on farms that adopted intensive mode of farming. The percentage occurrence of E. coli from waste disposal sources was lesser than that from fresh poultry droppings. Highest percentage of antibiotic resistance to the fluoroquinolones was found while the carbapenemase recorded the lowest. Statistical analysis shows that antibiotic treatment in poultry and resistant profile of isolates to antibiotics are directly related. The percentage similarity of gene sequence with those from Gene Data Bank (≥99.29%) validates the identity of the isolates as E. coli. About, 60% of the sampled population had the qnrS gene with a band size of approximately 322 base pair. Besides, 40% of the sampled isolates possessed the blaCMY gene with a band size of approximately 460 base pair. Both genes co-existed in the chromosome of 15% of the sampled isolates sourced from poultry droppings and waste sources. Phylogenetic classification links the origin of isolates from waste disposal sources to poultry production sites. Besides, variant strains of multiple antibiotic resistant E. coli from poultry with antibiotic treatment were more diverse compared to those obtained from birds raised without antibiotics.&#x0D; Conclusion: The qnrS and blaCMY genes found in multiple antibiotic resistant E. coli mediated resistance to critically important antibiotics. The co-existence of these genes in variants strains of E. coli occupying different phylogenetic clusters suggests that antibiotics were widely used on the birds. Antibiotic treatment regimen in poultry may be responsible for the expression of antibiotic resistant genes found in the chromosome of the variant strains of E. coli.

Pattern classification in traditional Chinese medicine and precision medicine: a narrative review

Pattern classification in traditional Chinese medicine and precision medicine: a narrative review

Joint classification from the viewpoint of Avicenna and modern medicine.

The purpose of this study is historic and analytical review on joint classification in Canon of Medicine and contribution of Avicenna in joint anatomy. In this study, we searched correlated literature in PubMed and Google Scholar as well as Avicenna's Canon of Medicine. One of the most important sources of Persian Medicine literature is Avicenna's Canon of Medicine. This Book itself consists of five volume medical textbook. Most of the first volume of the Canon of Medicine is anatomy. Avicenna in 1000years ego, while describing bones and joints, divides the joints into three types of mobile, semi-mobile, immobilized or fixed, and cites each example. In one of the three divisions of the 10th edition of the Kelly's textbook of Rheumatology, the joints have been divided into three types and described in the same way. Avicenna established preservation of health and treatment of diseases based on anatomy. Therefore, Avicenna's functional approach to the division of joints is important both in terms of the history of medicine according to Avicenna's description a thousand years ago and in terms of content and structure do not differ compared to today's Classification in medicine and it's interesting.

Possibility of using of international classification of functioning in rehabilitation of patients with cataract

The problem of rehabilitation of patients with cataract, analysis of effectiveness of surgical treatment, restoration of professional activity and obtaining a high quality of life after cataract surgery is of great medical and social importance. The basic principles of modern medicine are patient orientation, personalized and multidisciplinary approach. Attention is focused not on an individual disease and its symptoms, but on the patient as a whole person with all life’s concerns and problems. The study of the quality of life (QOL) of the patients with cataract allows us to assess the influence of cataract and surgical treatment on the psychological, emotional state of the patient, his functioning in the social environment. However, cataract surgery is a rehabilitation treatment aimed at restoring functions lost due to cataract. International classification of functioning (ICF), limitation of vital activity and health is a generally accepted classification in rehabilitation medicine. In accordance with new trends, the possibility of using ICF for the study of visual impairments and limitation of vital activity in patients with cataract before and after surgical treatment is becoming relevant.

A Lightweight Convolutional Neural Network Architecture Applied for Bone Metastasis Classification in Nuclear Medicine: A Case Study on Prostate Cancer Patients.

Bone metastasis is among the most frequent in diseases to patients suffering from metastatic cancer, such as breast or prostate cancer. A popular diagnostic method is bone scintigraphy where the whole body of the patient is scanned. However, hot spots that are presented in the scanned image can be misleading, making the accurate and reliable diagnosis of bone metastasis a challenge. Artificial intelligence can play a crucial role as a decision support tool to alleviate the burden of generating manual annotations on images and therefore prevent oversights by medical experts. So far, several state-of-the-art convolutional neural networks (CNN) have been employed to address bone metastasis diagnosis as a binary or multiclass classification problem achieving adequate accuracy (higher than 90%). However, due to their increased complexity (number of layers and free parameters), these networks are severely dependent on the number of available training images that are typically limited within the medical domain. Our study was dedicated to the use of a new deep learning architecture that overcomes the computational burden by using a convolutional neural network with a significantly lower number of floating-point operations (FLOPs) and free parameters. The proposed lightweight look-behind fully convolutional neural network was implemented and compared with several well-known powerful CNNs, such as ResNet50, VGG16, Inception V3, Xception, and MobileNet on an imaging dataset of moderate size (778 images from male subjects with prostate cancer). The results prove the superiority of the proposed methodology over the current state-of-the-art on identifying bone metastasis. The proposed methodology demonstrates a unique potential to revolutionize image-based diagnostics enabling new possibilities for enhanced cancer metastasis monitoring and treatment.

A GPU-based residual network for medical image classification in smart medicine

The recent advance of high-performance computing techniques like graphics processing unit (GPU) enables large-scale deep learning models for medical image analytics in smart medicine. Smart medicine has made great progress by applying convolutional neural networks (CNNs) like ResNet and VGG-16 to medical image classification. However, various CNN models achieve very limited accuracy in some cases where multiple diseases are revealed in an X-ray image. This paper presents a variant ResNet model by replacing the global average pooling with the adaptive dropout for medical image classification. In order for the presented model to recognize multiple diseases (i.e., multi-label classification), we convert the multi-label classification to N binary classification by training the parameters of the presented model for N times. Finally, experiments are conducted on a GPU Cluster to evaluate the presented model on three datasets, namely Montgomery County chest X-ray set, Shenzhen X-ray set, and NIH chest X-ray set. The results show the presented model achieves a great performance improvement for medical image classification without a significant efficiency reduction compared to the traditional architecture and VGG-16.

Мethods of Machine Learning in Ophthalmology: Review

Machine learning is applied in every field of human activity using digital data. In recent years, many papers have been published concerning artificial intelligence use in classification, regression and segmentation purposes in medicine and in ophthalmology, in particular. Artificial intelligence is a subsection of computer science and its principles, and concepts are often incomprehensible or used and interpreted by doctors incorrectly. Diagnostics of ophthalmology patients is associated with a significant amount of medical data that can be used for further software processing. By using of machine learning methods, it’s possible to find out, identify and count almost any pathological signs of diseases by analyzing medical images, clinical and laboratory data. Machine learning includes models and algorithms that mimic the architecture of biological neural networks. The greatest interest in the field is represented by artificial neural networks, in particular, networks based on deep learning due to the ability of the latter to work effectively with complex and multidimensional databases, coupled with the increasing availability of databases and performance of graphics processors. Artificial neural networks have the potential to be used in automated screening, determining the stage of diseases, predicting the therapeutic effect of treatment and the diseases outcome in the analysis of clinical data in patients with diabetic retinopathy, age-related macular degeneration, glaucoma, cataracts, ocular tumors and concomitant pathology. The main characteristics were the size of the training and validation datasets, accuracy, sensitivity, specificity, AUROC (Area Under Receiver Operating Characteristic Curve). A number of studies investigate the comparative characteristics of algorithms. Many of the articles presented in the review have shown the results in accuracy, sensitivity, specificity, AUROC, error values that exceed the corresponding indicators of an average ophthalmologist. Their introduction into routine clinical practice will increase the diagnostic, therapeutic and professional capabilities of a clinicians, which is especially important in the field of ophthalmic oncology, where there is a patient survival matter.

Privacy Protected Medical Data Classification in precision medicine using an Ontology-based Support Vector Machine in the Diabetes Management System

Diabetes is a serious health issue across the globe. As stated by the International Diabetes Foundation, currently 425 million people live with diabetes globally, and another 300 million people are expecting to be at higher risk of diabetes in the year 2030.Hence, there is an urgent clinical need for an early prognosis, diagnosis, and management of diabetes and its complications. In this context, numerous intelligent machine learning and data mining approaches including Support vector machine (SVM) have been exploited for diabetes management.SVM is a prevailing supervised and discriminative data classifier which assists the healthcare and biomedical professionals to ascertain unknown data patterns by training a large volume of real-time data. Since this database is the private asset, the sensitive information should be safeguarded without compromising the utility. Even though SVM is a fast and accurate machine learning technique, it does not have the capacity to represent semantically the classification and reasoning rules which can enable more precise classification. Therefore, the objective of this research is three-fold. Firstly, in order to protect sensitive clinical data, we introduce the Kronecker product and Crow Search Algorithm (CSA) based coefficient generation technique. Secondly, we design diabetes ontology to define domain concepts and relationships and allow medical datamining. Finally, we implement the Ontology-based Support Vector Machine (Ont-SVM) classifier by assimilating privacy protection, ontology, and SVM-based classifier for the diagnosis of diabetes.The experimental results on a real-world dataset, the Pima Indian database at the UCI repository, illustrate that the proposed Ont-SVM outperforms other existing approaches in terms of privacy, accuracy, specificity, and sensitivity.

SGL-SVM: A novel method for tumor classification via support vector machine with sparse group Lasso

At present, with the in-depth study of gene expression data, the significant role of tumor classification in clinical medicine has become more apparent. In particular, the sparse characteristics of gene expression data within and between groups. Therefore, this paper focuses on the study of tumor classification based on the sparsity characteristics of genes. On this basis, we propose a new method of tumor classification—Sparse Group Lasso (least absolute shrinkage and selection operator) and Support Vector Machine (SGL-SVM). Firstly, the primary selection of feature genes is performed on the normalized tumor datasets using the Kruskal–Wallis rank sum test. Secondly, using a sparse group Lasso for further selection, and finally, the support vector machine serves as a classifier for classification. We validate proposed method on microarray and NGS datasets respectively. Formerly, on three two-class and five multi-class microarray datasets it is tested by 10-fold cross-validation and compared with other three classifiers. SGL-SVM is then applied on BRCA and GBM datasets and tested by 5-fold cross-validation. Satisfactory accuracy is obtained by above experiments and compared with other proposed methods. The experimental results show that the proposed method achieves a higher classification accuracy and selects fewer feature genes, which can be widely applied in classification for high-dimensional and small-sample tumor datasets. The source code and all datasets are available at https://github.com/QUST-AIBBDRC/SGL-SVM/.

Deciphering Potential Correlations between New Biomarkers and Pattern Classification in Chinese Medicine by Bioinformatics: Two Examples of Rheumatoid Arthritis.

Biological complexity and the need for personalized medicine means that biomarker development has become increasingly challenging. Thus, new paradigms for research need to be created that bring together a different classifier of individuals. One potential solution is collaboration between biomarker development and Chinese medicine pattern classification. In this article, two examples of rheumatoid arthritis are discussed, including a new biomarker candidate casein kinase 2 interacting protein 1 (CKIP-1) and a micro RNA 214. The authors obtained a "snapshot" of pattern classification with disease in biomarker identification. Bioinformatics analyses revealed underlying biological functions of two biomarker candidates, in varying degrees, are correlated with Chinese medicine pattern of rheumatoid arthritis. The authors' initial attempt can provide a new window for studying the win-win potential correlation between the biomarkers and pattern classification in Chinese medicine.

Advanced Satellite Image Classification of Various Resolution Image Using a Novel Approach of Deep Neural Network Classifier

Image registration is computationally intensive and applied in a variety of applications, for example, multispectral classification, change recognition, climate prediction and multi-view analysis in GIS and medicine. There are three types of registration namely multi-view, multimodal and multi-temporal. In multi-view based registration, the images of the same scene taken at different viewpoints are analyzed and modeled for the requirement. Hence stereoscopic image sequences of the same view are acquired, and accurate comparison for the image classification is essential. This paper presents a robust method which has three steps. The first phase includes obtaining hyper spectral (satellite) images and preprocessing of them, the second period subdivides into image blocks for alignment, and the final step focuses on classification based on hyper graph structure using deep learning approach. For processing of satellite images, a new method linear iterative clustering and deep neural network classification are employed. Previous works in remote sensing applications involve training samples and hence prior knowledge of image sets which incurs more computational time. The implementation of this method shows an automatic, achieving better accuracy and dynamic reconfigurable image registration in reduced complexity. The mathematical model used is hidden markov chain model for clustering which provides region-wise feature construction for evaluating region shape and contextual information. The work yields classification accuracy of 94.12% which is far better than past outcomes in this engaged field of research. The execution of the usage is examined, a comparison is additionally influenced regarding false classification ratio, time complexity and clustering accuracy is demonstrated.

초음파영상검사와 한의변증분류와의 관계와 관련된 중의학 임상연구에 대한 문헌고찰

This study was to investigate the relationship between ultrasonographic examination and pattern identification classification on cinical studies in chinese medicine. We searched clinical studies related correlation between ultrasonographic examination and pattern identification classification in chinese medicine, that published from 2013 to 2016 in China National Knowledge Infrastructure (CNKI) databases by keywords,

Subtype classification and heterogeneous prognosis model construction in precision medicine.

Common diseases including cancer are heterogeneous. It is important to discover disease subtypes and identify both shared and unique risk factors for different disease subtypes. The advent of high-throughput technologies enriches the data to achieve this goal, if necessary statistical methods are developed. Existing methods can accommodate both heterogeneity identification and variable selection under parametric models, but for survival analysis, the commonly used Cox model is semiparametric. Although finite-mixture Cox model has been proposed to address heterogeneity in survival analysis, variable selection has not been incorporated into such semiparametric models. Using regularization regression, we propose a variable selection method for the finite-mixture Cox model and select important, subtype-specific risk factors from high-dimensional predictors. Our estimators have oracle properties with proper choices of penalty parameters under the regularization regression. An expectation-maximization algorithm is developed for numerical calculation. Simulations demonstrate that our proposed method performs well in revealing the heterogeneity and selecting important risk factors for each subtype, and its performance is compared to alternatives with other regularizers. Finally, we apply our method to analyze a gene expression dataset for ovarian cancer DNA repair pathways. Based on our selected risk factors, the prognosis model accounting for heterogeneity consistently improves the prediction for the survival probability in both training and test datasets.

Multicriteria methods for identifying patterns in the analysis of the flow of “dangerous financial documents”

The article outlines a concept of applying the methodology for identifying patterns used for detecting documents suggesting the execution of some criminal financial transactions. The analogies of diagnostic processes for disease classification in medicine were used in the method. The idea of the described method consists in defining model patterns of financial documents, suggesting criminal activity in the form of the financial flow and developing mathematical models of actual financial documents which shall be used for the comparisons with the patterns at a later stage of the process. The next step is to develop similarity indicators of documents to appropriate patterns, to define and develop a multicriteria detection area for the documents and to develop a method for dividing the set of monitored documents into similar document classes. The final stage is the development of multicriteria rankings that allow to organize the set of transaction documents according to the degree of similarity to the relevant patterns and to determine the optimal cut-off threshold in the ranking of documents intended for a more detailed analysis. The described method may be used in counteracting financial crimes, and in particular in combating money laundering.