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Assessing Lung Injury Induced by Streptozotocin-induced Diabetes: A Deep Neural Network Analysis of Histopathological
Tuğba Şentürk1, Demet Bolat2, Arzu Yay2,3
1Department of Biomedical Engineering, Inonu University, Malatya, Türkiye.
Introduction:
Diabetes mellitus is an endocrine disorder characterized by metabolic abnormalities and chronic hyperglycemia, caused by insulin deficiency (Type I) or resistance (Type II). It affects various tissues differently, and its complications extend beyond classical targets, such as the kidneys and eyes, to lesser-studied organs, including the lungs. Understanding tissue-specific damage is crucial for effective disease management and the prevention of complications.
Objective:
This study aims to evaluate the histopathological and immunohistochemical effects of diabetic lung fibrosis using a streptozotocin (STZ)-induced diabetes model. Additionally, it seeks to develop a high-performance image classification system based on deep neural networks to accurately classify tissue damage in diabetic models.
Methods:
Lung tissue samples were collected from the STZ-induced diabetes model and analyzed through histopathological and immunohistochemical techniques. Image data were further processed using convolutional neural networks (CNNs), including pre-trained models, such as ResNet50, VGG16, and SqueezeNet. Classification was conducted in multiple color spaces (RGB, Grayscale, and HSV) and evaluated using performance metrics, including confusion matrix, precision, recall, F1 score, and accuracy.
Results And Discussion:
The use of color significantly enhanced image patch classification performance. Among the models tested, SqueezeNet in the RGB color space demonstrated the highest accuracy, achieving an F1 score of 93.49% ± 0.04 and an accuracy of 93.77% ± 0.04. These results indicated the efficacy of CNN-based classification in detecting lung damage associated with diabetes.
Conclusion:
Our findings confirmed that diabetes induces histopathological changes in lung tissue, contributing to fibrosis and potential pulmonary complications. Deep learning-based classification methods, particularly when utilizing color space variations and advanced preprocessing techniques, provide a powerful tool for analyzing diabetic tissue damage and may aid in the development of diagnostic support systems.
