Authors:
Vincenzo Taormina
1
;
Donato Cascio
2
;
Leonardo Abbene
2
and
Giuseppe Raso
2
Affiliations:
1
Engineering Department, University of Palermo, viale delle Scienze, Palermo, Italy
;
2
Department of Physics and Chemistry, University of Palermo, viale delle Scienze, Palermo, Italy
Keyword(s):
Autoimmune Diseases, IIF Test, HEp-2 Images, Deep Learning, CNN, Fine Tuning, ROC Curve.
Abstract:
The classification of HEp-2 images, conducted through Indirect ImmunoFluorescence (IIF) gold standard method, in the positive / negative classes, is the first step in the diagnosis of autoimmune diseases. Since the test is often difficult to interpret, the research world has been looking for technological features for this problem. In recent years the methods of deep learning have overcome the other machine learning techniques in their effectiveness and robustness, and now they prevail in artificial intelligence studies. In this context, CNNs have played a significant role especially in the biomedical field. In this work we analysed the capabilities of CNN for fluorescence classification of HEp-2 images. To this end, the GoogLeNet pre-trained network was used. The method was developed and tested using the public database A.I.D.A. For the analysis of pre-trained network, the two strategies were used: as features extractors (coupled with SVM classifiers) and after fine-tuning. Performa
nce analysis was conducted in terms of ROC (Receiver Operating Characteristic) curve. The best result obtained with the fine-tuning method showed an excellent ability to discriminate between classes, with an area under the ROC curve (AUC) of 98.4% and an accuracy of 93%. The classification result using the CNN as features extractor obtained 97.3% of AUC, showing a difference in performance between the two strategies of little significance.
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