Sensors, Vol. 20, Pages 6711: Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation

Sensors, Vol. 20, Pages 6711: Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation Sensors doi: 10.3390/s20236711 Authors: 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 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 c...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research