Internet of Medical Things-Based on Deep Learning Techniques for Segmentation of Lung and Stroke Regions in CT Scans

• Published in: IEEE access 2020, Vol.8, p.71117-71135
• Main Authors: Han, Tao, Nunes, Virginia Xavier, De Freitas Souza, Luis Fabricio, Marques, Adriell Gomes, Silva, Iagson Carlos Lima, Junior, Marcos Aurelio Araujo Ferreira, Sun, Jinghua, Filho, Pedro P. Reboucas
• Format: Article
• Language: English
• Subjects: Classification; classification and segmentation; Computed tomography; Computer vision; CTs lung and stroke; Deep learning; Diagnostic systems; Diseases; fine-tuning; Health of things; Hemorrhaging; Image analysis; Image classification; Image segmentation; Internet of medical things; Lung; Machine learning; Medical diagnostic imaging; Medical imaging; Model accuracy; transfer learning; Vision systems
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2987932

The classification and segmentation of pathologies through intelligent systems is a significant challenge for medical image analysis and computer vision systems. Diseases, such as lung problems and strokes, have a serious effect on human health worldwide. Lung diseases are among the leading causes of death worldwide, lagging behind strokes that in 2016 became the second leading cause of death from illnesses. Computed tomography (CT) is one of the main clinical diagnostic exams, linked to Computerized Diagnostic Assistance Systems (CAD), which are becoming solutions for health technologies. In this work, we propose a method based on the health of things for the classification and segmentation of CT images of the lung and hemorrhagic stroke. The system called HTSCS - Medical Images: Health-of-Things System for the Classification and Segmentation of Medical Images, uses transfer learning between models based on deep learning combined with classical methods for fine-tuning. The proposed method obtained excellent results for the classification of hemorrhagic stroke and pulmonary regions, with values of up to 100% accuracy. The models also achieved outstanding performances for segmentation, with Accuracy above 99 % and Dice coefficient above 97% in the best cases with an average segmentation time between 0.095 and 1.7 seconds. To validate our approach, we compared our best models for the segmentation of lung and hemorrhagic stroke in CTs, with related works found in state of the art. Our method brings an innovative approach to classification and segmentation through the use of the Health of Things for different types of medical images with promising results for medical image analysis and computer vision fields.