Erythemato-squamous Diseases Research Articles

Classification and Prediction of Erythemato-Squamous Diseases Through Tensor-Based Learning

The paper proposes a classification algorithm based on support tensor machines which finds the maximum margin between the tensor spaces. The proposed algorithm has been deployed to classify erythemato-squamous diseases (ESDs) with the help of its features. Features are derived from the skin lesion images of ESDs, and it has been represented as second-order tensors, i.e., $$ \varvec{X} \in \varvec{ }{\mathbb{R}}^{\varvec{n}} $$ can be transformed into $$ \varvec{X} \in \,\varvec{ }{\mathbf{\Re }}^{{\varvec{n}_{1} }} \,\varvec{ } \otimes \,{\mathbf{\Re }}^{{\varvec{n}_{2} }} $$ where $$ n_{1} \times n_{2} \cong n $$. After deriving the features from the skin lesion images, dominant features are extracted using Tucker tensor decomposition method. Most of the existing machine learning algorithms depend on the vector-based learning models, and these algorithms suffer from the data overfitting problem. To resolve this problem, in this paper, tensor-based learning is implemented for classification. Proposed algorithm is evaluated with the real-time dataset (Xie et al. in: He, Liu, Krupinski, Xu (eds) Health information science, Springer, Berlin, 2012), and higher classification accuracy of 99.93–100% is achieved. The acquired results are compared with the existing machine learning algorithms, and it drives home the point that the proposed algorithm provides higher classification accuracy.

Fuzzy Neural System Application to Differential Diagnosis of Erythemato-Squamous Diseases

Fuzzy Neural System Application to Differential Diagnosis of Erythemato-Squamous Diseases

Mycosis fungoides: reflectance confocal microscopy and its role in the diagnosis

BackgroundMycosis fungoides (MF), a T-cell lymphoma, is the most common variant of primary cutaneous lymphomas. The discrete clinical aspect of the early patch-stage and an overlapping clinical presentation with other erythematosquamous skin diseases make the diagnosis of MF challenging. Histopathology is the gold standard for the diagnosis of MF. However, in early stages, MF can simulate inflammatory diseases, presenting reactive lymphocytes and absence of lymphocytic cytologic atypia. Consecutive biopsies are often necessary from several clinically suspect sites. Previous studies evaluated Reflectance Confocal Microscopy (RCM) as a promising tool for MF diagnosis because it offers the opportunity to analyze skin structures non-invasively at a ‘quasi-histopathologic’ resolution and to guide the most representative site for biopsy.Case presentationWe present a 76 years-old woman with a 5-year history of erythematous plaques and patches. She had had 5 previous skin biopsies with a pattern suggestive of nummular eczema. The history and the clinical presentation led to the suspicion of MF. RCM examination was performed and two lesions were marked for biopsy. The histopathology exam confirmed the diagnosis of MF. The patient was submitted to topical treatment with total remission of the lesions.ConclusionsThe mean time from onset of symptoms to diagnosis is 6 years and the delay in diagnosis results in delay for adequate therapy. Therefore, the selection of the biopsy site is crucial to avoid unrepresentative samples. Mancebo et al. recently published the largest cohort of cutaneous T.cell lymphoma examined with RCM. 83 lesions were evaluated, and the authors confirmed that this image technique was effective in to guide the most representative site for the biopsy. We believe that further and larger studies will clarify it and defining a diagnostic algorithm for the RCM diagnosis of MF.

Support Vector Machine Optimized by Elephant Herding Algorithm for Erythemato-Squamous Diseases Detection

Machine learning algorithms are used in numerous field and medicine is one of them. Automatic diagnosis or detection of different diseases based on list of symptoms can drastically improve and speedup diagnostics process. Determining diagnosis at earlier stages gives better healing results. In this paper a method for automatic erythemato-squamous diseases classification was proposed. Six erythemato-squamous diseases that are very hard to distinguish were classified by the optimized support vector machine. Recent swarm intelligent algorithm, elephant herding optimization algorithm was used to find optimal parameters for the support vector machine that was then used to determine the exact erythemato-squamous diseases. We compared accuracy of our proposed method to other approaches from literature using standard dataset and it obtained better results in all experiments.

Estimation of automatic detection of erythemato-squamous diseases through AdaBoost and its hybrid classifiers

This paper focuses on efficient techniques based on AdaBoost and its hybrid (AdaBoost-SVM) classifiers for an automatic detection of the erythemato-squamous diseases (ESD). The classification of ESD requires an enormous amount of computational effort, as all the features of the diseases have more than 90 % commonality. Some of the approaches reported in literature are fuzzy logic, artificial neural networks, neuro-fuzzy models and support vector machine. Recently, AdaBoost and its hybrid algorithms are widely used to enhance the accuracy of the learning algorithms. In this paper, we propose AdaBoost and its hybrid algorithms for diagnosis of erythemato-squamous diseases for the first time. User-friendly-interface is designed to assist dermatologists to estimate the ESD with the help of 34 features includes patient's histopathological and clinical data sets. Efficient interface design is based on the following steps: (1) feature selection for all the 34 parameters involved in the erythemato-squamous disease is done using association rule based Apriori algorithm, (2) association rules are used to find $$\upalpha \%$$?% of transactions (diseases) to meet $$\upbeta \%$$β% of the features of the diseases, (3) support value of all the subset of $$\upalpha $$? transaction from $$\upalpha \%$$?% of transactions is found and the dominant subset is found using Apriori algorithm, and (4) AdaBoost and its hybrid classifiers are used to classify the diseases. In this paper, the real, modest, gentle and hybrid AdaBoost algorithms are implemented and tested with the dataset of 366 patients. The data preprocessing reduces the dimensionality of the dataset and improves the time complexity of the estimation of the diseases. Proposed methodologies are proved as a potential solution for diagnosing ESD with significant improvement in computational time and accuracy compared to other models discussed in the recent literature.

The Erythemato-Squamous Dermatology Diseases Severity Determination using Self-Organizing Map

A new approach based on the implementation of Self Organizing Map is presented for automated detection of erythemato-squamous diseases. The purpose of clustering techniques is in order to determinate the severity of erythemato-squamous dermatology diseases. The studied domain contained records of patients with known diagnosis. Self-Organizing Map algorithm's task was to classify the data points, in this case the patients with attribute data, to one of the six clusters (psoriasis, seboreic dermatitis, lichen planus, pityriasis rosea, cronic dermatitis, dan pityriasis rubra pilaris). The algorithm was used to detect the six erythemato-squamous diseases when 33 features defining five disease indications were used. The purpose is to determine an optimum classification scheme for this problem. The present research demonstrated that the features well represent the erythemato-squamous diseases and SOM algorithm's task achieved high classification accuracies. The best accuration for psoriasis 85,94%, seboreic dermatitis 40,48%, lichen planus 56,25%, and pityriasis rosea 82,61%, with learning rate value were 0,1, 0,2, 0,9, and 0,4

Filter Based Feature Selection for Automatic Detection of Erythemato-squamous Diseases

Filter Based Feature Selection for Automatic Detection of Erythemato-squamous Diseases

Random forests for automatic differential diagnosis of erythemato-squamous diseases

Erythemato–squamous diseases (ESD) are frequent skin diseases that share some clinical features of erythema and scaling. Their automatic diagnosis was tackled using several approaches that achieved high performance accuracy. However, they generally remained unattractive for dermatologists because of the lack of direct readability of their output models. Decision trees are easy to understand, but their performance and structure are very sensitive to data changes. Ensembles of decision trees were introduced to reduce the effect of these problems, but on the expense of interpretability. In this paper, we present the results of our investigation of random forests and boosting as ensemble methods for the differential diagnosis of ESD. Experiments on clinical and histopathological data showed that the random forest outperformed the other ensemble classifiers in terms of accuracy, sensitivity and specificity. Its diagnosis accuracy, attaining more than 98%, was also better than those of classifiers based on genetic programming, genetic algorithms and k–means clustering.

Classification of Erythemato-Squamous Diseases Using Association Rules and Fuzzy c-Means Clustering

Automatic classification of the erythemato-squamous diseases is an important problem in dermatology. Despite very little differences, almost all erythemato-squamous diseases have similar clinical features of erythema and scaling. Thus, a hybrid classification model is proposed for automatic classification of the erythemato-squamous diseases. This hybrid model consists of two sub steps. In the first sub step, a feature selection schema is considered. In this context, the association rules (ARs) method is employed for feature selection purposes. The Apriori algorithm is considered as mining the ARs. The fuzzy c-means (FCM) clustering algorithm is located in the second step of the proposed hybrid model where the membership degrees of the data points are obtained through iterative minimization of a cost function. Several experimentations are conducted for evaluating the performance of the proposed hybrid method on detection of the erythemato-squamous diseases on MATLAB environment. We also compared our proposal with the standard FCM and k-means clustering algorithms. The performance evaluation of the proposed method was realized according to the several criterions such as classification accuracy, sensitivity and specificity values. According to the performance evaluation criterions, our proposal yielded better classification performance than the compared clustering methods. While the proposed AR+FCM obtained 75.96% classification accuracy, FCM and k-means produced 75.14 and 68.85% classification accuracies, respectively. Based on the obtained results, the proposed hybrid scheme improves the correct classification rate of erythemato-squamous diseases.

A Survey on Utilization of the Machine Learning Algorithms for the Prediction of Erythemato Squamous Diseases

The aim of this study list the contributions of various machine learning algorithms for the prediction of Erythemato Squamous Diseases (ESDs) and it is very useful for the budding researchers to do research in this field. In the advent of ozone depletion the ultra violet radiation is the major cause of many skin diseases, which are leading to skin cancer. Early detection of skin cancer is more important to avoid human loses and especially the white skinned people are more affected. The Asian and African race people are less affected as they have melanin in their skin. The American's are directly and more widely affected by the ozone depletion, due to this ESD, which is predominant among the skin diseases. Due to technology advancements a large amount of data are deposited. In these data the information is hidden as raw data and with latest methodologies and technologies like Data Mining, neural networks, fuzzy systems, Genetic and Evolutionary computing a pattern can be evolved to study them. Guvenir et al. (1998) studied about ESDs and contributed 366 patients data with 34 features consisting of clinical and histopathological data in the dermatology dataset (The data taken from School of Medicine in Gazi University and the department of Computer Science in Bilkent University, Turkey; and it is available in the URL (http://archive.ics.uci.edu/ml/datasets/Dermatology) in the year 1998. This survey study gives a brief description about the contribution of what in the field of ESDs in Chronological order from the year 1998 till 2013. In this study we intend to contribute various machine learning algorithms dealing with ESDs.

Action fuzzy rule based classifier for analysis of dermatology databases

In healthcare industries, the classification systems are applied to localise primary tumours, prognosis and recurrence of breast cancer, diagnosis of thyroid diseases, and rheumatology. The major requirements for such a system are prediction accuracy and comprehensibility of the learned knowledge by human experts. Success of a classification learning algorithm, in terms of these criteria, is directly related to the scheme used for representing the learned classification knowledge. An Adaptive Neuro Fuzzy Inference System (ANFIS) mechanism is proposed for erythemato-squamous disease analysis and to manage the knowledge acquired and found from the system. In addition, this system utilises Action Rule based Diagnostic System (ARDS) action and fuzzy rule based classifiers in accordance with the severity levels of the diseases. Empirical results endorse that this system is an authentic and reliable tool to reduce the human errors and improve the quality of medical care provided to the public.

An efficient approach to an automatic detection of erythemato-squamous diseases

This paper presents a novel approach to automatic detection of the erythemato-squamous diseases based on fuzzy extreme learning machine (FELM). Enormous computational efforts are required to classify these erythemato-squamous diseases. Some of the approaches performed previously are through fuzzy logic, artificial neural networks and neuro-fuzzy models. FELM-based differential diagnosis of these diseases involves decisions made by fuzzy logic and extreme learning machine (ELM) with greater efficiency in both time and accuracy. In this paper, we develop a user-friendly interface and this tool will be useful for a dermatologist to estimate the six types of erythemato-squamous diseases with the help of patient's histopathological and clinical data. Then, the developed interface is derived inbuilt using neural networks, adaptive neuro-fuzzy inference system and FELM. A dataset containing records of 366 patients with 34 features that define six disease characteristics was taken, of which 310 records were used as training data and 56 other records used as testing data. The dataset was preprocessed to obtain fuzzy values as input to get more accurate results in FELM. Given a training set of such records, ELM approach is applied. By combining fuzzy logic and ELM, more accurate results with increased performance are obtained with less computational efforts. Finally, the proposed FELM model proves to be a potential solution for the diagnosis of erythemato-squamous diseases with significant improvement in computational time and accuracy compared with other models discussed in the recent literature.

Two-stage hybrid feature selection algorithms for diagnosing erythemato-squamous diseases.

This paper proposes two-stage hybrid feature selection algorithms to build the stable and efficient diagnostic models where a new accuracy measure is introduced to assess the models. The two-stage hybrid algorithms adopt Support Vector Machines (SVM) as a classification tool, and the extended Sequential Forward Search (SFS), Sequential Forward Floating Search (SFFS), and Sequential Backward Floating Search (SBFS), respectively, as search strategies, and the generalized F-score (GF) to evaluate the importance of each feature. The new accuracy measure is used as the criterion to evaluated the performance of a temporary SVM to direct the feature selection algorithms. These hybrid methods combine the advantages of filters and wrappers to select the optimal feature subset from the original feature set to build the stable and efficient classifiers. To get the stable, statistical and optimal classifiers, we conduct 10-fold cross validation experiments in the first stage; then we merge the 10 selected feature subsets of the 10-cross validation experiments, respectively, as the new full feature set to do feature selection in the second stage for each algorithm. We repeat the each hybrid feature selection algorithm in the second stage on the one fold that has got the best result in the first stage. Experimental results show that our proposed two-stage hybrid feature selection algorithms can construct efficient diagnostic models which have got better accuracy than that built by the corresponding hybrid feature selection algorithms without the second stage feature selection procedures. Furthermore our methods have got better classification accuracy when compared with the available algorithms for diagnosing erythemato-squamous diseases.

A fast approach for detection of erythemato-squamous diseases based on extreme learning machine with maximum relevance minimum redundancy feature selection

In this paper, a novel hybrid method, which integrates an effective filter maximum relevance minimum redundancy (MRMR) and a fast classifier extreme learning machine (ELM), has been introduced for diagnosing erythemato-squamous (ES) diseases. In the proposed method, MRMR is employed as a feature selection tool for dimensionality reduction in order to further improve the diagnostic accuracy of the ELM classifier. The impact of the type of activation functions, the number of hidden neurons and the size of the feature subsets on the performance of ELM have been investigated in detail. The effectiveness of the proposed method has been rigorously evaluated against the ES disease dataset, a benchmark dataset, from UCI machine learning database in terms of classification accuracy. Experimental results have demonstrated that our method has achieved the best classification accuracy of 98.89% and an average accuracy of 98.55% via 10-fold cross-validation technique. The proposed method might serve as a new candidate of powerful methods for diagnosing ES diseases.

Genetic algorithm wrapped Bayesian network feature selection applied to differential diagnosis of erythemato-squamous diseases

This paper presents a new method for differential diagnosis of erythemato-squamous diseases based on Genetic Algorithm (GA) wrapped Bayesian Network (BN) Feature Selection (FS). With this aim, a GA based FS algorithm combined in parallel with a BN classifier is proposed. Basically, erythemato-squamous dataset contains six dermatological diseases defined with 34 features. In GA-BN algorithm, GA makes a heuristic search to find most relevant feature model that increase accuracy of BN algorithm with the use of a 10-fold cross-validation strategy. The subsets of features are sequentially used to identify six dermatological diseases via a BN fitting the corresponding data. The algorithm, in this case, produces 99.20% classification accuracy in the diagnosis of erythemato-squamous diseases. The strength of feature model generated for BN is furthermore tested with the use of Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Simple Logistics (SL) and Functional Decision Tree (FT). The resultant classification accuracies of algorithms are 98.36%, 97.00%, 98.36% and 97.81% respectively. On the other hand, BN algorithm with classification accuracy of 99.20% is quite a high diagnosis performance for erythemato-squamous diseases. The proposed algorithm makes no more than 3 misclassifications out of 366 instances. Furthermore, FS power of GA is also compared with two alternative search algorithms, i.e. Best First (BF) and Sequential Floating (SF). The obtained results have all together shown that the proposed GA-BN based FS and prediction strategy is very promising in diagnosis of erythemato-squamous diseases.

Automatic detection of erythemato-squamous diseases using PSO–SVM based on association rules

In this paper, we develop a diagnosis model based on particle swarm optimization (PSO), support vector machines (SVMs) and association rules (ARs) to diagnose erythemato-squamous diseases. The proposed model consists of two stages: first, AR is used to select the optimal feature subset from the original feature set; then a PSO based approach for parameter determination of SVM is developed to find the best parameters of kernel function (based on the fact that kernel parameter setting in the SVM training procedure significantly influences the classification accuracy, and PSO is a promising tool for global searching). Experimental results show that the proposed AR_PSO–SVM model achieves 98.91% classification accuracy using 24 features of the erythemato-squamous diseases dataset taken from UCI (University of California at Irvine) machine learning database. Therefore, we can conclude that our proposed method is very promising compared to the previously reported results.

Feature selection using binary particle swarm optimization and support vector machines for medical diagnosis.

In this article, we propose a feature selection strategy using a binary particle swarm optimization algorithm for the diagnosis of different medical diseases. The support vector machines were used for the fitness function of the binary particle swarm optimization. We evaluated our proposed method on four databases from the machine learning repository, including the single proton emission computed tomography heart database, the Wisconsin breast cancer data set, the Pima Indians diabetes database, and the Dermatology data set. The results indicate that, with selected less number of features, we obtained a higher accuracy in diagnosing heart, cancer, diabetes, and erythematosquamous diseases. The results were compared with the traditional feature selection methods, namely, the F-score and the information gain, and a superior accuracy was obtained with our method. Compared to the genetic algorithm for feature selection, the results of the proposed method show a higher accuracy in all of the data, except in one. In addition, in comparison with other methods that used the same data, our approach has a higher performance using less number of features.

Detection of erythemato-squamous diseases using AR-CatfishBPSO-KSVM

Nowadays, one of the most important usages of machine learning is diagnosis of diverse diseases. In this work, we introduces a diagnosis model based on Catfish binary particle swarm optimization (CatfishBPSO), kernelized support vector machines (KSVM) and association rules (AR) as our feature selection method to diagnose erythemato-squamous diseases. The proposed model consisted of two stages. In the first stage, AR is used to select the optimal feature subset from the original feature set. Next, based on the fact that kernel parameter setting in the SVM training procedure significantly influences the classification accuracy and CatfishBPSO is a promising tool for global searching, a CatfishBPSO based approach is employed for parameter determination of KSVM. Experimental results show that the proposed AR-CatfishBPSO-KSVM model achieves 99.09% classification accuracy using 24 features of the erythemato-squamous disease dataset which shows that our proposed method is more accurate compared to other popular methods in this literature like Support vector machines and AR-MLP (association rules multilayer perceptron). It should be mentioned that we took our dataset from University of California Irvine machine learning database.

Cytokine network in psoriasis revisited

Psoriasis is a chronic genetically determined, erythemato-squamous disease associated with many comorbidities. Evidence from clinical studies and experimental models support the concept that psoriasis is a Tcell-mediated inflammatory skin disease and Thelper (Th) cells -Th1, Th17 and Th22- play an important role in the pathogenesis. Th1 cytokines IFNγ, IL-2, as well as Th17 cytokines IL-17A, IL-17F, IL-22, IL-26, and TNFα (Th1 and Th17 cytokine) are increased in serum and lesional skin. IL-22 produced by Th17 and new subset of T helper cells, Th22, is also increased within psoriatic lesions and in the serum. Other recently recognized cytokines of significant importance in psoriasis are IL-23, IL-20 and IL-15. The IL-23/Th17 pathway plays a dominant role in psoriasis pathogenesis. Currently due to enormous methodological progress, more and more clinical and histopathological psoriatic features could be explained by particular cytokine imbalance, which still is one of the most fascinating dermatological research fields stimulating new and new generations of researchers.

Instance-based classifiers applied to medical databases: Diagnosis and knowledge extraction

The aim of this paper is to study the feasibility and the performance of some classifier systems belonging to family of instance-based (IB) learning as second-opinion diagnostic tools and as tools for the knowledge extraction phase in the process of knowledge discovery in clinical databases. We consider three clinical databases: one relating to the differential diagnosis of erythemato-squamous diseases, the second to the diagnosis of the onset of diabetes mellitus and the third dealing with a problem of diagnostic imaging in nuclear cardiology. We apply five IB classifiers to each database; two are based on exemplars, one is based on prototypes and two are hybrid. One of the latter classifiers is a new classifier introduced here and is called prototype exemplar learning classifier (PEL-C). We use cross-validation techniques to evaluate and compare the performances of several classifier systems as diagnostic tools, considering indexes such as accuracy, sensitivity, specificity, and conciseness of class representations. Moreover we analyze the number and the type of instances that represent the diagnostic classes learnt by each classifier to evaluate and compare their knowledge extraction capabilities. An examination of the experimental results shows that classifiers with the best classification performances are the optimized k-nearest neighbour classifier (k-NNC) and PEL-C. The k-NNC uses the highest number of representative instances, 100% of the entire database, whereas PEL-C uses a far lesser number of representative instances: equal, on the average, to the 3% of the database. As tools for knowledge extraction, we interpret the kind of class representations obtained by IB classifiers as a form of nosological knowledge. Additionally, we report the most interesting diagnostic class representations to be those extracted by PEL-C because they are composed of a mixture of abstracted prototypical cases (syndromes) and selected atypical clinical cases. This study shows that IB methods - most notably, the optimized k-NNC and the PEL-C - can be used and may be advantageous for clinical decision support systems and that IB classifiers can be used for nosological knowledge extraction. Because PEL-C uses more compact and potentially meaningful class descriptions, it is preferable when the diagnostic problem at-hand needs smaller storage space or for knowledge extraction itself. The complexity and responsibility of diagnostic practice requires that these results be confirmed further within other clinical domains.