2020
DOI: 10.3390/s20236711
|View full text |Cite
|
Sign up to set email alerts
|

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

Abstract: 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 Tomograp… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1

Citation Types

0
9
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
8
1

Relationship

0
9

Authors

Journals

citations
Cited by 17 publications
(9 citation statements)
references
References 69 publications
0
9
0
Order By: Relevance
“…In [ 22 ], the authors propose an efficient SVM for lung classification, wherein the SVM’s characteristics are adjusted, and features are extracted using a refined gray wolf optimization algorithm in combination with a genetic algorithm (GWO-GA). The experimental phase is classified into three categories: parameterization testing, feature engineering, and optimal SVM.…”
Section: Related Workmentioning
confidence: 99%
“…In [ 22 ], the authors propose an efficient SVM for lung classification, wherein the SVM’s characteristics are adjusted, and features are extracted using a refined gray wolf optimization algorithm in combination with a genetic algorithm (GWO-GA). The experimental phase is classified into three categories: parameterization testing, feature engineering, and optimal SVM.…”
Section: Related Workmentioning
confidence: 99%
“…Deep learning has become a powerful tool for processing medical images due to its strong data self-learning ability and is widely used in various image segmentation tasks, including for brain tissue, bone, lung and blood vessels [19][20][21][22][23][24]. The segmentation function of deep learning can accurately locate the position of the target area and determine the shape and contour of the target by recognising the internal pixels or edges of region of interest (ROI) in medical images.…”
Section: Introductionmentioning
confidence: 99%
“…Therefore, the emergence of the IoT becomes a milestone in the field of the digital medical domain. Medical diagnosis under the smart IoT environment can send physiological information and medical signals through communication networks to monitoring systems for analyzing and diagnosing with the aid of artificial intelligence techniques [ 5 , 6 , 7 ]. Consequently, it is beneficial for improving clinical medical services and lowering management costs, so that it can help to make better lifestyle and disease prevention plans and personalized medical services for patients [ 8 , 9 ].…”
Section: Introductionmentioning
confidence: 99%