Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation
Several pathologies have a direct impact on society, causing public health problems. Pulmonary diseases such as Chronic obstructive pulmonary disease (COPD) are already the third leading cause of death in the world, leaving tuberculosis at ninth with 1.7 million deaths and over 10.4 million new occu...
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doaj-f522f0cd627441ca9f63eadc3d9627b62020-11-27T07:54:52ZengMDPI AGSensors1424-82202020-11-01206711671110.3390/s20236711Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT SegmentationLuís Fabrício de Freitas Souza0Iágson Carlos Lima Silva1Adriell Gomes Marques2Francisco Hércules dos S. Silva3Virgínia Xavier Nunes4Mohammad Mehedi Hassan5Victor Hugo C. de Albuquerque6Pedro P. Rebouças Filho7Department of Computer Science, Federal Institute of Education, Science and Technology of Ceará, Fortaleza CE 60040-215, BrazilDepartment of Computer Science, Federal Institute of Education, Science and Technology of Ceará, Fortaleza CE 60040-215, BrazilDepartment of Computer Science, Federal Institute of Education, Science and Technology of Ceará, Fortaleza CE 60040-215, BrazilDepartment of Computer Science, Federal Institute of Education, Science and Technology of Ceará, Fortaleza CE 60040-215, BrazilDepartment of Computer Science, Federal Institute of Education, Science and Technology of Ceará, Fortaleza CE 60040-215, BrazilInformation Systems Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi ArabiaDepartment of Computer Science, Federal Institute of Education, Science and Technology of Ceará, Fortaleza CE 60040-215, BrazilDepartment of Computer Science, Federal Institute of Education, Science and Technology of Ceará, Fortaleza CE 60040-215, BrazilSeveral pathologies have a direct impact on society, causing public health problems. Pulmonary diseases such as Chronic obstructive pulmonary disease (COPD) are already the third leading cause of death in the world, leaving tuberculosis at ninth with 1.7 million deaths and over 10.4 million new occurrences. The detection of lung regions in images is a classic medical challenge. Studies show that computational methods contribute significantly to the medical diagnosis of lung pathologies by Computerized Tomography (CT), as well as through Internet of Things (IoT) methods based in the context on the health of things. The present work proposes a new model based on IoT for classification and segmentation of pulmonary CT images, applying the transfer learning technique in deep learning methods combined with Parzen’s probability density. The proposed model uses an Application Programming Interface (API) based on the Internet of Medical Things to classify lung images. The approach was very effective, with results above 98% accuracy for classification in pulmonary images. Then the model proceeds to the lung segmentation stage using the Mask R-CNN network to create a pulmonary map and use fine-tuning to find the pulmonary borders on the CT image. The experiment was a success, the proposed method performed better than other works in the literature, reaching high segmentation metrics values such as accuracy of 98.34%. Besides reaching 5.43 s in segmentation time and overcoming other transfer learning models, our methodology stands out among the others because it is fully automatic. The proposed approach has simplified the segmentation process using transfer learning. It has introduced a faster and more effective method for better-performing lung segmentation, making our model fully automatic and robust.https://www.mdpi.com/1424-8220/20/23/6711deep learningmask R-CNNfine-tuningtransfer learningImage lung segmentation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Luís Fabrício de Freitas Souza Iágson Carlos Lima Silva Adriell Gomes Marques Francisco Hércules dos S. Silva Virgínia Xavier Nunes Mohammad Mehedi Hassan Victor Hugo C. de Albuquerque Pedro P. Rebouças Filho |
spellingShingle |
Luís Fabrício de Freitas Souza Iágson Carlos Lima Silva Adriell Gomes Marques Francisco Hércules dos S. Silva Virgínia Xavier Nunes Mohammad Mehedi Hassan Victor Hugo C. de Albuquerque Pedro P. Rebouças Filho Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation Sensors deep learning mask R-CNN fine-tuning transfer learning Image lung segmentation |
author_facet |
Luís Fabrício de Freitas Souza Iágson Carlos Lima Silva Adriell Gomes Marques Francisco Hércules dos S. Silva Virgínia Xavier Nunes Mohammad Mehedi Hassan Victor Hugo C. de Albuquerque Pedro P. Rebouças Filho |
author_sort |
Luís Fabrício de Freitas Souza |
title |
Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation |
title_short |
Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation |
title_full |
Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation |
title_fullStr |
Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation |
title_full_unstemmed |
Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation |
title_sort |
internet of medical things: an effective and fully automatic iot approach using deep learning and fine-tuning to lung ct segmentation |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2020-11-01 |
description |
Several pathologies have a direct impact on society, causing public health problems. Pulmonary diseases such as Chronic obstructive pulmonary disease (COPD) are already the third leading cause of death in the world, leaving tuberculosis at ninth with 1.7 million deaths and over 10.4 million new occurrences. The detection of lung regions in images is a classic medical challenge. Studies show that computational methods contribute significantly to the medical diagnosis of lung pathologies by Computerized Tomography (CT), as well as through Internet of Things (IoT) methods based in the context on the health of things. The present work proposes a new model based on IoT for classification and segmentation of pulmonary CT images, applying the transfer learning technique in deep learning methods combined with Parzen’s probability density. The proposed model uses an Application Programming Interface (API) based on the Internet of Medical Things to classify lung images. The approach was very effective, with results above 98% accuracy for classification in pulmonary images. Then the model proceeds to the lung segmentation stage using the Mask R-CNN network to create a pulmonary map and use fine-tuning to find the pulmonary borders on the CT image. The experiment was a success, the proposed method performed better than other works in the literature, reaching high segmentation metrics values such as accuracy of 98.34%. Besides reaching 5.43 s in segmentation time and overcoming other transfer learning models, our methodology stands out among the others because it is fully automatic. The proposed approach has simplified the segmentation process using transfer learning. It has introduced a faster and more effective method for better-performing lung segmentation, making our model fully automatic and robust. |
topic |
deep learning mask R-CNN fine-tuning transfer learning Image lung segmentation |
url |
https://www.mdpi.com/1424-8220/20/23/6711 |
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