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

Han, Tao; Nunes, Virgínia Xavier; Souza, Luís Fabrício de F.; Marques, Adriell Gomes; Silva, Iágson Carlos Lima; Ferreira, Marcos Vinicius; Sun, Jinghua; Filho, Pedro Pedrosa Rebouças

doi:10.1109/access.2020.2987932

Cited by 50 publications

(22 citation statements)

References 73 publications

(106 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Depending on factors of interest, such as the material and hardness of the subject, various types of images, such as CT images or X-ray radiographic images, can be used to analyze the subject. Various studies, such as medicine, agriculture, and manufacturing, are actively being conducted [25][26][27][28][29][30][31]. These images have the advantage of seeing changes in parameters of interest (e.g., materials constituting the object, hardness, and height) from a photograph of the object at a glance [32].…”

Section: Depth Imagementioning

confidence: 99%

Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing

Kang

Kim

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

This paper introduces and implements an efficient training method for deep learning–based anomaly area detection in the depth image of a tire. A depth image of 16 bit integer size is used in various fields, such as manufacturing, industry, and medicine. In addition, the advent of the 4th Industrial Revolution and the development of deep learning require deep learning–based problem solving in various fields. Accordingly, various research efforts use deep learning technology to detect errors, such as product defects and diseases, in depth images. However, a depth image expressed in grayscale has limited information, compared with a three-channel image with potential colors, shapes, and brightness. In addition, in the case of tires, despite the same defect, they often have different sizes and shapes, making it difficult to train deep learning. Therefore, in this paper, the four-step process of (1) image input, (2) highlight image generation, (3) image stacking, and (4) image training is applied to a deep learning segmentation model that can detect atypical defect data. Defect detection aims to detect vent spews that occur during tire manufacturing. We compare the training results of applying the process proposed in this paper and the general training result for experiment and evaluation. For evaluation, we use intersection of union (IoU), which compares the pixel area where the actual error is located in the depth image and the pixel area of the error inferred by the deep learning network. The results of the experiment confirmed that the proposed methodology improved the mean IoU by more than 7% and the IoU for the vent spew error by more than 10%, compared to the general method. In addition, the time it takes for the mean IoU to remain stable at 60% is reduced by 80%. The experiments and results prove that the methodology proposed in this paper can train efficiently without losing the information of the original depth data.

show abstract

Section: Depth Imagementioning

confidence: 99%

Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing

Kang

Kim

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Han et al [ 32 ] proposed a technique based on the health of things to perform the classification and segmentation of computed tomography images of the lung and hemorrhagic stroke. The IoT used in this work took the form of a tool for direct interaction with the user, which directly selects the best set of extractors and classifiers for the proposed problem.…”

Section: Related Workmentioning

confidence: 99%

“…Tools with the use of IoT in the health area are increasingly explored in recent studies. The use of these tools frequently assists medical diagnoses, such as the approach by Tao Han et al [ 32 ], which used IoT to detect lung and brain in CT images. The authors used a combination of networks and achieved good results with fine-tuning.…”

Section: Introductionmentioning

confidence: 99%

Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation

Souza

Silva²,

Marques³

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…The fine-grained arrangement is completed with a typical convolutional neural system (CNN). TAO HAN et.al, [5] Heewon Chung et.al, [6] Automatic Lung Segmentation with Juxta-Pleural Nodule Identification utilizing Active Contour Model and Bayesian Approach. They propose a novel lung division strategy to limit the juxta-pleural knob issue, an infamous test in the applications.…”

Section: Study Areamentioning

confidence: 99%

Lung CT Image Recognition using Deep Learning Techniques to Detect Lung Cancer

Chalasani¹

2020

IJETER

View full text Add to dashboard Cite

Now a days, The digital image recognition techniques are extensively used in a couple of therapeutic domains for image improvement in earlier division and treatment stages, where the time factor is basic to discover the variety from the standard issues in target pictures, especially in various threatening development tumors, for instance, and lung infection. Lung cancer diagnosis is the most important early-stage research field for researcher. The proposed system is designed for the premature detection of lung cancer in two stages. The proposed method consists of several steps such as image acquisition, preprocessing, trivialization, thresholding, segmentation, extraction of features, and detection of neural networks. At first, the lung CT images they're input into the system and then passed through the preprocessing phase of the image using some processing techniques. Lung cancer growth is the most significant sickness cause high death rate. Also, computer vision methods helped determination can be valuable for doctors to precisely recognize the malignant growth cells. Numerous computer vision supported strategies have been contemplated and applied utilizing image recognition and deep learning. To minimize their effect in classification, the JSRT data set is considered to be the most commonly used reference data set to perform experiments. The right segmentation of the lung tumor from X-rays, CT-scans or MRI are the steps towards an integrated diagnostic device for the detection of lung cancer To train this neural network using volumes with tumor size and position, our detection is. In recent techniques, such as machine learning or deep learning, lung cancer can be predicted but this technique is not appropriate for predicting image segmentation in that particular field.

show abstract

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

Cited by 50 publications

References 73 publications

Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing

Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing

Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation

Lung CT Image Recognition using Deep Learning Techniques to Detect Lung Cancer

Contact Info

Product

Resources

About