2,476 research outputs found
Evaluation of Statistical Features for Medical Image Retrieval
In this paper we present a complete system allowing the classification of medical images in order to detect possible diseases present in them. The proposed method is developed in two distinct stages: calculation of descriptors and their classification. In the first stage we compute a vector of thirty-three statistical features: seven are related to statistics
of the first level order, fifteen to that of second level where thirteen are calculated by means of co-occurrence matrices and two with absolute gradient; the last thirteen finally are calculated using run-length matrices. In the second phase, using the descriptors already calculated, there is the actual image classification. Naive Bayes, RBF, Support VectorMa-
chine, K-Nearest Neighbor, Random Forest and Random Tree classifiers are used. The results obtained from the proposed system show that the analysis carried out both on textured and on medical images lead to have a high accuracy
A Review on Classification of White Blood Cells Using Machine Learning Models
The machine learning (ML) and deep learning (DL) models contribute to
exceptional medical image analysis improvement. The models enhance the
prediction and improve the accuracy by prediction and classification. It helps
the hematologist to diagnose the blood cancer and brain tumor based on
calculations and facts. This review focuses on an in-depth analysis of modern
techniques applied in the domain of medical image analysis of white blood cell
classification. For this review, the methodologies are discussed that have used
blood smear images, magnetic resonance imaging (MRI), X-rays, and similar
medical imaging domains. The main impact of this review is to present a
detailed analysis of machine learning techniques applied for the classification
of white blood cells (WBCs). This analysis provides valuable insight, such as
the most widely used techniques and best-performing white blood cell
classification methods. It was found that in recent decades researchers have
been using ML and DL for white blood cell classification, but there are still
some challenges. 1) Availability of the dataset is the main challenge, and it
could be resolved using data augmentation techniques. 2) Medical training of
researchers is recommended to help them understand the structure of white blood
cells and select appropriate classification models. 3) Advanced DL networks
such as Generative Adversarial Networks, R-CNN, Fast R-CNN, and faster R-CNN
can also be used in future techniques.Comment: 23 page
Random Forest-Based Prediction of Stroke Outcome
[Abstract] We research into the clinical, biochemical and neuroimaging factors associated with the outcome of stroke patients to generate a predictive model using machine learning techniques for prediction of mortality and morbidity 3-months after admission. The dataset consisted of patients with ischemic stroke (IS) and non-traumatic intracerebral hemorrhage (ICH) admitted to Stroke Unit of a European Tertiary Hospital prospectively registered. We identified the main variables for machine learning Random Forest (RF), generating a predictive model that can estimate patient mortality/morbidity according to the following groups: (1) IS + ICH, (2) IS, and (3) ICH. A total of 6022 patients were included: 4922 (mean age 71.9 ± 13.8 years) with IS and 1100 (mean age 73.3 ± 13.1 years) with ICH. NIHSS at 24, 48 h and axillary temperature at admission were the most important variables to consider for evolution of patients at 3-months. IS + ICH group was the most stable for mortality prediction [0.904 ± 0.025 of area under the receiver operating characteristics curve (AUC)]. IS group presented similar results, although variability between experiments was slightly higher (0.909 ± 0.032 of AUC). ICH group was the one in which RF had more problems to make adequate predictions (0.9837 vs. 0.7104 of AUC). There were no major differences between IS and IS + ICH groups according to morbidity prediction (0.738 and 0.755 of AUC) but, after checking normality with a Shapiro Wilk test with the null hypothesis that the data follow a normal distribution, it was rejected with W = 0.93546 (p-value < 2.2e−16). Conditions required for a parametric test do not hold, and we performed a paired Wilcoxon Test assuming the null hypothesis that all the groups have the same performance. The null hypothesis was rejected with a value < 2.2e−16, so there are statistical differences between IS and ICH groups. In conclusion, machine learning algorithms RF can be effectively used in stroke patients for long-term outcome prediction of mortality and morbidity.This study was partially supported by grants from the Spanish Ministry of Science and Innovation (SAF2017-84267-R), Xunta de Galicia (Axencia Galega de Innovación (GAIN): IN607A2018/3), Instituto de Salud Carlos III (ISCIII) (PI17/00540, PI17/01103), Spanish Research Network on Cerebrovascular Diseases RETICS-INVICTUS PLUS (RD16/0019) and by the European Union FEDER program. T. Sobrino (CPII17/00027), F. Campos (CPII19/00020) are recipients of research contracts from the Miguel Servet Program (Instituto de Salud Carlos III). General Directorate of Culture, Education and University Management of Xunta de Galicia (ED431G/01,252 ED431D 2017/16), “Galician Network for Colorectal Cancer Research" (Ref. ED431D 2017/23), Competitive Reference Groups (ED431C 2018/49), Spanish Ministry of Economy and Competitiveness via funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13–3503), European Regional Development Funds (FEDER).Xunta de Galicia; IN607A2018/3Xunta de Galicia; ED431G/01,252Xunta de Galicia; ED431D 2017/1
Interpretable Survival Analysis for Heart Failure Risk Prediction
Survival analysis, or time-to-event analysis, is an important and widespread
problem in healthcare research. Medical research has traditionally relied on
Cox models for survival analysis, due to their simplicity and interpretability.
Cox models assume a log-linear hazard function as well as proportional hazards
over time, and can perform poorly when these assumptions fail. Newer survival
models based on machine learning avoid these assumptions and offer improved
accuracy, yet sometimes at the expense of model interpretability, which is
vital for clinical use. We propose a novel survival analysis pipeline that is
both interpretable and competitive with state-of-the-art survival models.
Specifically, we use an improved version of survival stacking to transform a
survival analysis problem to a classification problem, ControlBurn to perform
feature selection, and Explainable Boosting Machines to generate interpretable
predictions. To evaluate our pipeline, we predict risk of heart failure using a
large-scale EHR database. Our pipeline achieves state-of-the-art performance
and provides interesting and novel insights about risk factors for heart
failure
Analysis and automated classification of images of blood cells to diagnose acute lymphoblastic leukemia
Analysis of white blood cells from blood can help to detect Acute Lymphoblastic Leukemia, a potentially fatal blood cancer if left untreated. The morphological analysis of blood cells images is typically performed manually by an expert; however, this method has numerous drawbacks, including slow analysis, low precision, and the results depend on the operator’s skill. We have developed and present here an automated method for the identification and classification of white blood cells using microscopic images of peripheral blood smears. Once the image has been obtained, we propose describing it using brightness, contrast, and micro-contour orientation histograms. Each of these descriptions provides a coding of the image, which in turn provides n parameters. The extracted characteristics are presented to an encoder’s input. The encoder generates a high-dimensional binary output vector, which is presented to the input of the neural classifier. This paper presents the performance of one classifier, the Random Threshold Classifier. The classifier’s output is the recognized class, which is either a healthy cell or an Acute Lymphoblastic Leukemia-affected cell. As shown below, the proposed neural Random Threshold Classifier achieved a recognition rate of 98.3 % when the data has partitioned on 80 % training set and 20 % testing set for. Our system of image recognition is evaluated using the public dataset of peripheral blood samples from Acute Lymphoblastic Leukemia Image Database. It is important to mention that our system could be implemented as a computational tool for detection of other diseases, where blood cells undergo alterations, such as Covid-1
Factors associated to mortality in patients of the first wave infected by COVID-19 in Spain
This project aims to carrying out an in-depth, retrospective and multicen-ter analysis on the distribution, correlations, missing values and survival of covid-infected patients in Spain. Artificial intelligence (AI) has been used for extracting information about the factors involved in mortality, for classifying patients according to certain patterns, and for estimating the time for a group of individuals to experience an event of interest (e.g., reach a critical condition or require mechanical ventilation)
Predictive models for COVID-19 detection using routine blood tests and machine learning
The problem of accurate, fast, and inexpensive COVID-19 tests has been urgent till now. Standard COVID-19 tests need high-cost reagents and specialized laboratories with high safety requirements, are time-consuming. Data of routine blood tests as a base of SARS-CoV-2 invasion detection allows using the most practical medicine facilities. But blood tests give general information about a patient’s state, which is not directly associated with COVID-19. COVID-19-specific features should be selected from the list of standard blood characteristics, and decision-making software based on appropriate clinical data should be created. This review describes the abilities to develop predictive models for COVID-19 detection using routine blood tests and machine learning
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