BGNDL: arquitetura de deep learning para diferenciação da proteína biglycan em tecido mamário com e sem câncer

Abstract

Artificial Intelligence and Machine Learning have become important allies in healthcare. In this context, Deep Learning has provided support for critical medical tasks, including di agnosis, outcome prediction, and treatment response. Histological images, the focus of this work, come from the tissues of the human body. The diagnosis of many diseases, especially malignant diseases, depends on the evaluation of histological sections. Within the scenario of diagnostic imaging evaluations, variations exist. In the literature, it has been shown that de spite the consistency of results in the same rater, there is a difference between different raters. According to the literature, differences in visual perception and clinical training can lead to inconsistencies in diagnostic and prognostic opinions since pathological analysis is naturally subjective. Routine staining of tissues for microscopic study is not always sufficient. In these cases biological markers, the biomarkers, are used as complements. In this regard, the growing interest in biomarker research has increased due to rising research costs and the time required to develop a new compound. For these biomarkers to be used in research, it is necessary that they go through a validation process, where they need to be measured in a test system, where one of the properties, the sensitivity of the biomarker, will be evaluated in this work. With the exposure of this scenario, this work promoted, through Deep Learning, the creation of the CNN architecture that will check, from histological images with the biomarker Biglycan, if there is a difference between the expression of Biglycan between tissues with and without breast cancer . The association of Deep Learning and Biglycan protein expression by DAB staining intensity using color deconvolution is new and necessary for biomarker validation. In this sense, the main contributions of this work are: Creation of an original dataset of histological images with and without breast cancer that were subjected to the immunohistochemistry technique to de termine Biglycan protein expression, Automation of the color deconvolution model to analyze only images with DAB expression and Development of a CNN architecture that can determine whether there is a difference between Biglycan expression between tissues with and without breast cancer. The breast histology images were classified by an average percentage greater than 93%, indicating that there is a difference between Biglycan biomarker expression between tissues with and without breast cancer.