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.
According to the World Health Organization, severe lung pathologies bring about 250,000 deaths each year, and by 2030 it will be the third leading cause of death in the world. The usage of (CT) Computed Tomography is a crucial tool to aid medical diagnosis. Several studies, based on the computer vision area, in association with the medical field, provide computational models through machine learning and deep learning. In this study, we created a new feature extractor that works as the Mask R-CNN kernel for lung image segmentation through transfer learning. Our approaches minimize the number of images used by CNN’s training step, thereby also decreasing the number of interactions performed by the network. The model obtained results surpassing the standard results generated by Mask R-CNN, obtaining more than 99% about the metrics of real lung position on CT with our best model Mask + SVM, surpassing methods in the literature reaching 11 seconds for pulmonary segmentation. To present the effectiveness of our approach also in the generalization of models (methods capable of generalizing machine knowledge to other different databases), we carried out experiments also with various databases. The method was able, with only one training based on a single database, to segment CT lung images belonging to another lung database, generating excellent results getting 99% accuracy.
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