Abstract
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.
Highlights
Antinuclear antibodies (ANAs) are a very large category of autoantibodies, or antibodies that the body produces against itself
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 performance analysis showed an accuracy of 96.4% and a mean class accuracy equal to
Summary
Antinuclear antibodies (ANAs) are a very large category of autoantibodies, or antibodies that the body produces against itself. In recent scientific research on pattern recognition, deep learning methods and in particular the convolutional neural networks (CNNs) have been proven to be efficient and reliable models to achieve remarkable performance for image classification and object detection tasks [22]. Li H. et al [27] proposed a method for analyzing HEp-2 images based on the use of a CNN to construct a pattern histogram, and through this a linear SVM was trained. The system uses a pre-trained network, AlexNet [28], as a feature extractor and is able to classify the following six fluoroscopic patterns: Homogeneous, speckled, nucleolar, centromere, Golgi, and nuclear membrane. Different layers of the best known and used pre-trained network were evaluated as feature extractors for the problem of the classification of HEp-2 image patterns. For an effective performance comparison, the method was evaluated on a public data set issued by the 2014 ICPR Competition [14]
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