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

Cascio, Donato; Taormina, Vincenzo; Raso, G.

doi:10.3390/app9020307

Cited by 15 publications

(7 citation statements)

References 53 publications

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“…Recently, deep convolutional neural networks (CNNs) have demonstrated outstanding performance for generic visual recognition tasks. Donato et al [ 10 , 11 ] suggested to use nine pre-trained CNN models for feature extraction and classification for six different HEp-2 image patterns. The proposed methods have been applied to classification on benchmark datasets for HEp-2 cells.…”

Section: Introductionmentioning

confidence: 99%

Application of Supervised Machine Learning to Recognize Competent Level and Mixed Antinuclear Antibody Patterns Based on ICAP International Consensus

Sheu

Chung

et al. 2021

Diagnostics

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Background: Antinuclear antibody pattern recognition is vital for autoimmune disease diagnosis but labor-intensive for manual interpretation. To develop an automated pattern recognition system, we established machine learning models based on the International Consensus on Antinuclear Antibody Patterns (ICAP) at a competent level, mixed patterns recognition, and evaluated their consistency with human reading. Methods: 51,694 human epithelial cells (HEp-2) cell images with patterns assigned by experienced medical technologists collected in a medical center were used to train six machine learning algorithms and were compared by their performance. Next, we choose the best performing model to test the consistency with five experienced readers and two beginners. Results: The mean F1 score in each classification of the best performing model was 0.86 evaluated by Testing Data 1. For the inter-observer agreement test on Testing Data 2, the average agreement was 0.849 (?) among five experienced readers, 0.844 between the best performing model and experienced readers, 0.528 between experienced readers and beginners. The results indicate that the proposed model outperformed beginners and achieved an excellent agreement with experienced readers. Conclusions: This study demonstrated that the developed model could reach an excellent agreement with experienced human readers using machine learning methods.

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Section: Introductionmentioning

confidence: 99%

Application of Supervised Machine Learning to Recognize Competent Level and Mixed Antinuclear Antibody Patterns Based on ICAP International Consensus

Sheu

Chung

et al. 2021

Diagnostics

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“…Therefore, it is mandatory to apply a feature selection protocol to remove redundant and irrelevant features. There are various feature selection techniques available in the literature [19,20,21,22,23], however, inspired by recent studies [24,25,26], we applied a two-step feature selection procedure to check whether it was able to reduce feature dimensions and improve performance. In particular, the F-score algorithm for ranking features (present in each feature encoding) was employed, followed by a sequential forward search to find the optimal feature set (Figure 3).…”

Section: Resultsmentioning

confidence: 99%

mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides

Boopathi

Subramaniyam

Malik

et al. 2019

IJMS

156

109

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Anticancer peptides (ACPs) are promising therapeutic agents for targeting and killing cancer cells. The accurate prediction of ACPs from given peptide sequences remains as an open problem in the field of immunoinformatics. Recently, machine learning algorithms have emerged as a promising tool for helping experimental scientists predict ACPs. However, the performance of existing methods still needs to be improved. In this study, we present a novel approach for the accurate prediction of ACPs, which involves the following two steps: (i) We applied a two-step feature selection protocol on seven feature encodings that cover various aspects of sequence information (composition-based, physicochemical properties and profiles) and obtained their corresponding optimal feature-based models. The resultant predicted probabilities of ACPs were further utilized as feature vectors. (ii) The predicted probability feature vectors were in turn used as an input to support vector machine to develop the final prediction model called mACPpred. Cross-validation analysis showed that the proposed predictor performs significantly better than individual feature encodings. Furthermore, mACPpred significantly outperformed the existing methods compared in this study when objectively evaluated on an independent dataset.

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“…However, the analysis of IIF images, as a whole, and in particular, in regards to the analysis of intensity, is extremely complex and linked to the experience of the immunologist [4]. For this reason, in recent years there have been numerous scientific works aimed at obtaining automatic support systems for HEp-2 image analysis [5,6]. In particular, in the last few years, various research groups interested in the topic have tried to exploit the potential offered by recent machine learning techniques in order to address the problem of the classification of HEp-2 images.…”

Section: Introductionmentioning

confidence: 99%

Performance of Fine-Tuning Convolutional Neural Networks for HEp-2 Image Classification

et al. 2020

Self Cite

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The search for anti-nucleus antibodies (ANA) represents a fundamental step in the diagnosis of autoimmune diseases. The test considered the gold standard for ANA research is indirect immunofluorescence (IIF). The best substrate for ANA detection is provided by Human Epithelial type 2 (HEp-2) cells. The first phase of HEp-2 type image analysis involves the classification of fluorescence intensity in the positive/negative classes. However, the analysis of IIF images is difficult to perform and particularly dependent on the experience of the immunologist. For this reason, the interest of the scientific community in finding relevant technological solutions to the problem has been high. Deep learning, and in particular the Convolutional Neural Networks (CNNs), have demonstrated their effectiveness in the classification of biomedical images. In this work the efficacy of the CNN fine-tuning method applied to the problem of classification of fluorescence intensity in HEp-2 images was investigated. For this purpose, four of the best known pre-trained networks were analyzed (AlexNet, SqueezeNet, ResNet18, GoogLeNet). The classifying power of CNN was investigated with different training modalities; three levels of freezing weights and scratch. Performance analysis was conducted, in terms of area under the ROC (Receiver Operating Characteristic) curve (AUC) and accuracy, using a public database. The best result achieved an AUC equal to 98.6% and an accuracy of 93.9%, demonstrating an excellent ability to discriminate between the positive/negative fluorescence classes. For an effective performance comparison, the fine-tuning mode was compared to those in which CNNs are used as feature extractors, and the best configuration found was compared with other state-of-the-art works.

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

Cited by 15 publications

References 53 publications

Application of Supervised Machine Learning to Recognize Competent Level and Mixed Antinuclear Antibody Patterns Based on ICAP International Consensus

Application of Supervised Machine Learning to Recognize Competent Level and Mixed Antinuclear Antibody Patterns Based on ICAP International Consensus

mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides

Performance of Fine-Tuning Convolutional Neural Networks for HEp-2 Image Classification

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