A multi-process system for HEp-2 cells classification based on SVM

Classification of HEp-2 Staining Pattern Images Using Adapted Multilayer Perceptron Neural Network-Based Intra-Class Variation of Cell Shape

There exists a growing interest from the clinical practice research communities in the development of methods to automate HEp-2 stained cells classification procedure from histopathological images. Challenges faced by these methods include variations in cell densities and cell patterns, overfitting of features, large-scale data volume and stained cells. In this paper, a multi-class multilayer perceptron technique is adapted by adding a new hidden layer to calculate the variation in the mean, scale, kurtosis and skewness of higher order spectra features of the cell shape information. The adapted technique is then jointly trained and the probability of classification calculated using a Softmax activation function. This method is proposed to address overfitting, stained and large-scale data volume problems, and classify HEp-2 staining cells into six classes. An extensive experimental analysis is studied to verify the results of the proposed method. The technique has been trained and tested on the dataset from ICPR-2014 and ICPR-2016 competitions using the Task-1. The experimental results have shown that the proposed model achieved higher accuracy of 90.3% (with data augmentation) than of 87.5% (with no data augmentation). In addition, the proposed framework is compared with existing methods, as well as, the results of methods using in ICPR2014 and ICPR2016 competitions.The results demonstrate that our proposed method effectively outperforms recent methods.

Heterogeneous ensemble with information theoretic diversity measure for human epithelial cell image classification

In this work, we propose a heterogeneous committee (ensemble) of diverse members (classification approaches) to solve the problem of human epithelial (HEp-2) cell image classification using indirect Immunofluorescence (IIF) imaging. We hypothesize that an ensemble involving different feature representations can enable higher performance if individual members in the ensemble are sufficiently varied. These members are of two types: (1) CNN-based members, (2) traditional members. For the CNN members, we have employed the well-established ResNet, DenseNet, and Inception models, which have distinctive salient aspects. For the traditional members, we incorporate class-specific features which are characterized depending on visual morphological attributes, and some standard texture features. To select the members which are discriminating and not redundant, we use an information theoretic measure which considers the trade-off between individual accuracies and diversity among the members. For all selected members, a compelling fusion required to combine their outputs to reach a final decision. Thus, we also investigate various fusion methods that combine the opinion of the committee at different levels: maximum voting, product, decision template, Bayes, Dempster-Shafer, etc. The proposed method is evaluated using ICPR-2014 data which consists of more images than some previous datasets ICPR-2012 and demonstrate state-of-the-art performance. To check the effectiveness of the proposed methodology for other related datasets, we test our methodology with newly compiled large-scale HEp-2 dataset with 63K cell images and demonstrate comparable performance even with less number of training samples. The proposed method produces 99.80% and 86.03% accuracy respectively when tested on ICPR-2014 and a new large-scale data containing 63K samples. Graphical Abstract Overview of the proposed methodology.

Machine Learning and Feature Selection Methods for EGFR Mutation Status Prediction in Lung Cancer

The evolution of personalized medicine has changed the therapeutic strategy from classical chemotherapy and radiotherapy to a genetic modification targeted therapy, and although biopsy is the traditional method to genetically characterize lung cancer tumor, it is an invasive and painful procedure for the patient. Nodule image features extracted from computed tomography (CT) scans have been used to create machine learning models that predict gene mutation status in a noninvasive, fast, and easy-to-use manner. However, recent studies have shown that radiomic features extracted from an extended region of interest (ROI) beyond the tumor, might be more relevant to predict the mutation status in lung cancer, and consequently may be used to significantly decrease the mortality rate of patients battling this condition. In this work, we investigated the relation between image phenotypes and the mutation status of Epidermal Growth Factor Receptor (EGFR), the most frequently mutated gene in lung cancer with several approved targeted-therapies, using radiomic features extracted from the lung containing the nodule. A variety of linear, nonlinear, and ensemble predictive classification models, along with several feature selection methods, were used to classify the binary outcome of wild-type or mutant EGFR mutation status. The results show that a comprehensive approach using a ROI that included the lung with nodule can capture relevant information and successfully predict the EGFR mutation status with increased performance compared to local nodule analyses. Linear Support Vector Machine, Elastic Net, and Logistic Regression, combined with the Principal Component Analysis feature selection method implemented with 70% of variance in the feature set, were the best-performing classifiers, reaching Area Under the Curve (AUC) values ranging from 0.725 to 0.737. This approach that exploits a holistic analysis indicates that information from more extensive regions of the lung containing the nodule allows a more complete lung cancer characterization and should be considered in future radiogenomic studies.

Automatic Segmentation of Coronary Arteries in X-ray Angiograms using Multiscale Analysis and Artificial Neural Networks

This paper presents a novel method for the automatic segmentation of coronary arteries in X-ray angiograms, based on multiscale analysis and neural networks. The multiscale analysis is performed by using Gaussian filters in the spatial domain and Gabor filters in the frequency domain, which are used as inputs by a multilayer perceptron (MLP) for the enhancement of vessel-like structures. The optimal design of the MLP is selected following a statistical comparative analysis, using a training set of 100 angiograms, and the area under the ROC curve ( A z ) for assessment of the detection performance. The detection results of the proposed method are compared with eleven state-of-the-art blood vessel enhancement methods, obtaining the highest performance of A z = 0.9775 , with a test set of 30 angiograms. The database of 130 X-ray coronary angiograms has been outlined by a specialist and approved by a medical ethics committee. On the other hand, the vessel extraction technique was selected from fourteen binary classification algorithms applied to the multiscale filter response. Finally, the proposed segmentation method is compared with twelve state-of-the-art vessel segmentation methods in terms of six binary evaluation metrics, where the proposed method provided the most accurate coronary arteries segmentation with a classification rate of 0.9698 and Dice coefficient of 0.6857 , using the test set of angiograms. In addition to the experimental results, the performance in the detection and segmentation steps of the proposed method have also shown that it can be highly suitable for systems that perform computer-aided diagnosis in X-ray imaging.

Multiscale Entropy Analysis with Low-Dimensional Exhaustive Search for Detecting Heart Failure

Multiscale entropy (MSE) is widely used to analyze heartbeat signals. Even though cardiologists do not use MSE to diagnose heart failure at present, these studies are of importance and have potential clinical applications. In previous studies, MSE discrimination between old congestive heart failure (CHF) and healthy individuals has remained controversial. Few studies have been published on the discrimination between them, using only MSE with machine learning for automatic multidimensional analysis, with reported testing accuracies of less than 86%. In this study, we determined the optimal MSE scales for discrimination by using a low-dimensional exhaustive search along with three classifiers—linear discriminant analysis (LDA), support vector machine (SVM), and k-nearest neighbor (KNN). In younger people (<55 years), the results showed an accuracy of up to 95.5% with two optimal MSE scales (2D) and up to 97.7% with four optimal MSE scales (4D) in discriminating between young CHF and healthy participants. In older people (≥55 years), the discrimination accuracy reached 90.1% using LDA in 2D, SVM in 3D (three optimal MSE scales), and KNN in 5D (five optimal MSE scales). LDA with a 3D exhaustive search also achieved 94.4% accuracy in older people. Therefore, the results indicate that MSE analysis can differentiate between CHF and healthy individuals of any age.

Deep CNN for IIF Images Classification in Autoimmune Diagnostics

The diagnosis and monitoring of autoimmune diseases are very important problem in medicine. The most used test for this purpose is the antinuclear antibody (ANA) test. An indirect immunofluorescence (IIF) test performed by Human Epithelial type 2 (HEp-2) cells as substrate antigen is the most common methods to determine ANA. In this paper we present an automatic HEp-2 specimen system based on a convolutional neural network method able to classify IIF images. The system consists of a module for features extraction based on a pre-trained AlexNet network and a classification phase for the cell-pattern association using six support vector machines and a k-nearest neighbors classifier. The classification at the image-level was obtained by analyzing the pattern prevalence at cell-level. The layers of the pre-trained network and various system parameters were evaluated in order to optimize the process. This system has been developed and tested on the HEp-2 images indirect immunofluorescence images analysis (I3A) public database. To test the generalisation performance of the method, the leave-one-specimen-out procedure was used in this work. The performance analysis showed an accuracy of 96.4% and a mean class accuracy equal to 93.8%. The results have been evaluated comparing them with some of the most representative works using the same database.

Deep Convolutional Neural Network for HEp-2 Fluorescence Intensity Classification

Indirect ImmunoFluorescence (IIF) assays are recommended as the gold standard method for detection of antinuclear antibodies (ANAs), which are of considerable importance in the diagnosis of autoimmune diseases. Fluorescence intensity analysis is very often complex, and depending on the capabilities of the operator, the association with incorrect classes is statistically easy. In this paper, we present a Convolutional Neural Network (CNN) system to classify positive/negative fluorescence intensity of HEp-2 IIF images, which is important for autoimmune diseases diagnosis. The method uses the best known pre-trained CNNs to extract features and a support vector machine (SVM) classifier for the final association to the positive or negative classes. This system has been developed and the classifier was trained on a database implemented by the AIDA (AutoImmunité, Diagnostic Assisté par ordinateur) project. The method proposed here has been tested on a public part of the same database, consisting of 2080 IIF images. The performance analysis showed an accuracy of fluorescent intensity around 93%. The results have been evaluated by comparing them with some of the most representative state-of-the-art works, demonstrating the quality of the system in the intensity classification of HEp-2 images.

An Automatic HEp-2 Specimen Analysis System Based on an Active Contours Model and an SVM Classification

The antinuclear antibody (ANA) test is widely used for screening, diagnosing, and monitoring of autoimmune diseases. The most common methods to determine ANA are indirect immunofluorescence (IIF), performed by human epithelial type 2 (HEp-2) cells, as substrate antigen. The evaluation of ANA consist an analysis of fluorescence intensity and staining patterns. This paper presents a complete and fully automatic system able to characterize IIF images. The fluorescence intensity classification was obtained by performing an image preprocessing phase and implementing a Support Vector Machines (SVM) classifier. The cells identification problem has been addressed by developing a flexible segmentation methods, based on the Hough transform for ellipses, and on an active contours model. In order to classify the HEp-2 cells, six SVM and one k-nearest neighbors (KNN)classifiers were developed. The system was tested on a public database consisting of 2080 IIF images. Unlike almost all work presented on this topic, the proposed system automatically addresses all phases of the HEp-2 image analysis process. All results have been evaluated by comparing them with some of the most representative state-of-the-art work, demonstrating the goodness of the system in the characterization of HEp-2 images.