Abstract
Rhinology studies anatomy, physiology and diseases affecting the nasal region: one of the most modern techniques to diagnose these diseases is nasal cytology or rhinocytology, which involves analyzing the cells contained in the nasal mucosa under a microscope and researching of other elements such as bacteria, to suspect a pathology. During the microscopic observation, bacteria can be detected in the form of biofilm, that is, a bacterial colony surrounded by an organic extracellular matrix, with a protective function, made of polysaccharides. In the field of nasal cytology, the presence of biofilm in microscopic samples denotes the presence of an infection. In this paper, we describe the design and testing of interesting diagnostic support, for the automatic detection of biofilm, based on a convolutional neural network (CNN). To demonstrate the reliability of the system, alternative solutions based on isolation forest and deep random forest techniques were also tested. Texture analysis is used, with Haralick feature extraction and dominant color. The CNN-based biofilm detection system shows an accuracy of about 98%, an average accuracy of about 100% on the test set and about 99% on the validation set. The CNN-based system designed in this study is confirmed as the most reliable among the best automatic image recognition technologies, in the specific context of this study. The developed system allows the specialist to obtain a rapid and accurate identification of the biofilm in the slide images.
Highlights
Artificial intelligence and in particular machine learning has played a fundamental role in the medical field, providing important support to doctors, especially for assisted diagnosis by means of computer aided diagnosis (CAD) systems
CAD systems have recently become an integral part of clinical diagnosis processes and medical images evaluation
Regardless of what happens with automatic diagnosis systems, CAD systems only play a role of support, and their performances are not supposed to be comparable to the ones of the specialized doctors and not even replace them, only playing a complementary role [1,2,3]
Summary
Image recognition algorithms found successful usage in several diagnostic practices, mostly in diagnostic imaging [4,5,6,7,8,9]. Deep learning models, such as convolutional neural network (CNN), are used for blood cell classification [10,11,12], cytometry [13], and to diagnose brain [14,15,16,17], colon-rectal [18]
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