Computer-Assisted Classification Patterns in Autoimmune Diagnostics: The AIDA Project

Elgaaïed, Amel Benammar; Cascio, Donato; Bruno, Salvatore; Ciaccio, Maria Cristina; Cipolla, Marco; Fauci, Alessandro; Morgante, Rossella; Taormina, Vincenzo; Gorgi, Yousr; Triki, Raja Marrakchi; Ahmed, Mélika Ben; Louzir, Hechmi; Yalaoui, Sadok; Sfar, I.; Issaoui, Yassine; Abidi, Ahmed; Ammar, M.; Bedhiafi, Walid; Fraj, Oussama Ben; Bouhaha, R.; Hamdi, Khouloud; Soumaya, K.; Neili, Bilel; Gati, Asma; Lucchese, Mariano; Catanzaro, Maria; Barbara, Vincenza; Brusca, Ignazio; Fregapane, Maria; Amato, Gaetano; Friscia, Giuseppe; Neila, Trai; Turkia, Souayeh; Haouami, Youssra; Rekik, Raja; Bouokez, Hayet; Simone, M. Vasile; Fauci, F.; Raso, G.

doi:10.1155/2016/2073076

Cited by 21 publications

(18 citation statements)

References 19 publications

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“…As mentioned in [39], the classification accuracy and mean class accuracy (MCA) are used to evaluate the performance of the algorithm. CCR k is first defined as the correct classification rate for class k, as shown in Eq.…”

Section: Performance Measurementsmentioning

confidence: 99%

Automatic Recognition of Tea Diseases Based on Deep Learning

Chen¹,

Jia²

2020

Advances in Forest Management Under Global Change

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With the rapid development of intelligent agriculture and precision agriculture, computer image processing technology has been widely used to solve various problems in the agricultural field. In particular, the advantages of convolutional neural networks (CNNs) in image classification have also been widely used in the automatic recognition and classification of plant diseases. In this paper, a deep convolutional neural network named LeafNet capable of recognizing the seven types of diseases from tea leaf disease images was established, with an accuracy of up to 90.23%, aiming to provide timely and accurate diagnostic services in the remote and topographic tea plantation in China. At the same time, the traditional machine learning algorithm is applied for comparative analysis, which extracts the dense scale-invariant feature transform (DSIFT) of the image and constructs the bag of visual word (BOVW) model to express the image based on the DSIFT descriptor. The support vector machines (SVMs) and multilayer perceptron (MLP) were used to identify tea leaf diseases, with an accuracy of 60.91 and 70.94%, respectively.

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Section: Performance Measurementsmentioning

confidence: 99%

Automatic Recognition of Tea Diseases Based on Deep Learning

Chen¹,

Jia²

2020

Advances in Forest Management Under Global Change

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“…To evaluate the performance of a binary segmentation method, the accuracy metric has been the most commonly used in literature. However, because accuracy metric is low sensitive to imbalanced classes, in this work the metrics of mean class accuracy [20], sensitivity, specificity, positive predictive value, and Dice similarity coefficient are also employed to evaluate the segmentation performance of the proposed method. The six metrics are in the range [0, 1].…”

Section: Performance Evaluationmentioning

confidence: 99%

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

et al. 2019

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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.

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“…Elgaaied Benammar et al [24] have optimized and tested a CAD system on HEp-2 images which is able to recognize seven fluorescence patterns. The system searches and classifies positive and negative mitosis, within the image; the classification of mitosis occurs by using two neural network classifiers, the final decision-making process for the detection of fluorescence pattern is achieved by using a K-Nearest Neighbors classifier.…”

Section: Related Workmentioning

confidence: 99%

“…The development of a CAD system is intimately linked to a data collection. In this work the dataset provided by the AIDA (AutoImmunité, Diagnostic Assisté par ordinateur) project [24] was used. In this project, using a uniform approach, seven immunology services (three Tunisian and four Sicilian) contributed to collect images of the IIF test on HEp-2 cells.…”

Section: Databasementioning

confidence: 99%

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

2019

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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.

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Computer-Assisted Classification Patterns in Autoimmune Diagnostics: The AIDA Project

Cited by 21 publications

References 19 publications

Automatic Recognition of Tea Diseases Based on Deep Learning

Automatic Recognition of Tea Diseases Based on Deep Learning

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

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

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