Multi-categorical classification using deep learning applied to the diagnosis of gastric cancer

• Published in: Jornal brasileiro de patologia e medicina laboratorial Vol. 56
• Main Authors: Kloeckner, Jonas, Sansonowicz, Tatiana K., Rodrigues, Áttila L., Nunes, Tatiana W. N.
• Format: Journal Article
• Language: English
• Published: Sociedade Brasileira de Patologia Clínica 2020
• Subjects: deep learning/machine learning model; gastric neoplasms; MEDICAL LABORATORY TECHNOLOGY; MEDICINE, RESEARCH & EXPERIMENTAL; neural networks (computer); PATHOLOGY; aprendizaje profundo/modelo de aprendizaje de máquinas; neural networks (computer); deep learning/machine learning model; redes neurais (computação); aprendizagem profunda/modelo de aprendizagem de máquina; neoplasias gástricas; gastric neoplasms; redes neurales/neuronales (computación)
• ISSN: 1676-2444; 1678-4774; 1678-4774
• DOI: 10.5935/1676-2444.20200013

ABSTRACT Introduction: Pathologists currently face a substantial increase in workload and complexity of their diagnosis work on different types of cancer. This is due to the increased incidence and detection of neoplasms, associated with diagnostic subspecialization and the advent of personalized medicine. There are numerous treatments available for different types of cancer, and the diagnosis must be dispensed quickly and accurately for each case. Deep learning is a tool that has been used in daily life, including image detection, and there is growing interest in its application in Medicine and especially in Pathology, where it has a revolutionary potential. Objective: In this article, we present deep learning, in particular convolutional neural networks, as a potential technique for the analysis of digitized images of histopathological slides, detecting identifiable patterns in an automated manner, introducing the possibility of applying this technology as an auxiliary tool in the diagnosis of neoplasms, especially in gastric cancer, the object of this preliminary study. Method: From a database of digitized images of histopathological slides representative of gastric cancer, we identified three morphological patterns of neoplasia, as well as non-neoplastic tissue patterns, with which we train a convolutional neural network algorithm, designed to identify and categorize similar images within these standards, in an automated manner. Results: The results of identification and automatic classification in the defined categories were satisfactory, with ROC curves above 0.9. Conclusion: The results show the potential application of convolutional neural networks for digitized slides of gastric cancer, in accordance with international literature findings.