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In this paper, we address the lack of interpretability of Support Vector Machine (SVM) via rules based on support vectors. The lack of intuitive explanation of the rules in the domain of medical whole slide image classification by determining a reduced subset of Scale-Invariant Feature Transform (SIFT) features and linear dimensionality reduction. This reduced subset of SIFT features that participate...
Data science methods have the potential to benefit other scientific fields by shedding new light on common questions. One such task is help to make predictions on medical data. Diabetes mellitus or simply diabetes is a disease caused due to the increase level of blood glucose. Various traditional methods, based on physical and chemical tests, are available for diagnosing diabetes. The methods strongly...
The major distinguishing features used to assess a data hiding technique are distortion and embedding capacity. This paper presents a comparison between classical Prediction error expansion based reversible watermarking and proposed prediction error expansion scheme considering region of interest for grayscale medical images. In classical prediction error expansion the augmentation of the predicted...
The volume of academic paper submissions and publications is growing at an ever increasing rate. While this flood of research promises progress in various fields, the sheer volume of output inherently increases the amount of noise. We present a system to automatically separate papers with a high from those with a low likelihood of gaining citations as a means to quickly find high impact, high quality...
This paper presents an improved reversible data hiding algorithm using digital images based on the histogram shifting technique. Proposed method can accurately recover the original image and extract the hidden data accurately. The highest two peak values of the host image's histogram are selected for data hiding. This embedding process is repeated again and again, to attain larger embedding capacity...
In order to solve the problem of complicated calculation and frequent human intervention, a new method for extracting the centerline of blood vessel based on magnetic resonance angiography (MRA) is proposed in this paper. First of all, according to the different diameter of blood vessels, the multi-scale filtering algorithm is utilized for enhancing the different scales of blood vessels by Hessian...
Feature selection reduces a data set into a subset which also represents the entire data with less computational complexity and performance does not affect much. However, to extract such a subset is a nontrivial task although there are a number of methods to handle this problem. In the near past an approach based on rough set have been used for feature selection. The dependency measure is one of the...
Sequential patterns mining from data is a well stated data mining problem. It has a number of applications such as DNA sequencing, signal processing, speech analysis etc. In this problem, it is require to mine the causal relationship between different events. An event is a non-empty disordered collection of items. One of the important applications of sequential pattern mining is in medical data. Sequential...
Data mining techniques are used for mining useful trends or patterns from textual and image data sets. Medical data mining is very important field as it has significant utility in healthcare domain in the real world. The mining techniques can help the healthcare industry to improve quality of services and grow faster with state-of-the-art technologies. Technology usage is not limited to decision making...
This paper proposes an active mining process for improvement of quality of clinical process by using service logs in a hospital information system. First, datasets of temporal change of the number of orders are extracted from service logs stored in hospital information system. Then, since datsets of temporal change can be viewed as time-series of a statistic, clustering can be applied to the data...
Respiratory gating is a powerful tool for tackling motion-related issues in chest PET imaging. On current scanners the respiratory signal is obtained from external devices, whereas with Data-Driven methods it can be extracted directly from the data. The aim of this work is to show the increased potential of the application of Principal Component Analysis (PCA) on TOF data. We propose a methodology...
In this paper, an image segmentation method is presented to analyze the clusters of Computed Tomography (CT) image. Target image is divided to small parts called as observation screens. Principal Component Analysis (PCA) is used for better representation of features about observation screens. The optimal number of component related with observation screen is determined by Horn's Parallel Analysis...
Medical files and observation papers have always been an important source of knowledge. Unfortunately, most of the times they are still stored as physical documents either printed or handwritten, thus making it difficult to transfer this precious information from one place to another, or centralizing and extracting new knowledge from it. But with nowadays advancements in computer science this problem...
An optimization classification algorithm for MRI images of premature brain injury is introduced. Based on the shortcomings of the classical ID3 algorithm in dealing with the continuous attributes of medical image, the new algorithm selects the testing feature by comparing the information gain ratio and adds the handling methods for filling null values. Then it discrete the continuous attributes by...
Large medical image data sets with high dimensionality require substantial amount of computation time for data creation and data processing. This paper presents a novel generalized method that finds optimal image-based feature sets that reduce computational time complexity while maximizing overall classification accuracy for detection of diabetic retinopathy (DR). First, region-based and pixel-based...
Aiming at the particularity of MRI images, the ARC algorithm which is suitable for medical image processing is present. The new method introduces the bi-support association rules based on FP-tree. In the process of generating the rule of association class and constructing the FP-tree, the maximum support is introduced on the premise of minimal support. It can make the support of discovered rules be...
Clinical environment is very complex, and flexible and adaptive service improvement is crucial in maintaining quality of medical care. Thus, incremental update of software service in a hospital information system (HIS) and its evaluation is important. This paper introduces an active mining process for development of a an embedded software in which service logs stored in HIS are used to calculate the...
We propose a new outlier generation approach for one-class random forests (OCRF), a recently developed one-class classifier. The proposed method makes use of a positive and unlabeled learning (PUL) algorithm to generate outliers from the unlabeled samples. The outlier samples generated and the target samples are then used to train an OCRF classifier for one-class classification. The proposed method...
Telemedicine has increased the number of ways in which healthcare can be delivered across places and countries instead of requiring the provider and the recipient to be present in the same place. One application of telemedicine is the exchange of medical images between remotely located healthcare entities. However, a major obstacle telemedicine faces is providing confidentiality, integrity, and authenticity...
Big Data analytics is basically the process of analyzing and mining of Big Data which can produce business and operational knowledge at an unprecedented specificity and scale. The paper focuses on the applications and challenges of Big Data Analytics in the healthcare industry. The requirement of analyzing and leveraging clinical data collected by different sources is one of the crucial drivers for...
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