Cloud-Based Lung Tumor Detection and Stage Classification Using Deep Learning Techniques.
Artificial intelligence (AI), Internet of Things (IoT), and the cloud computing have recently become widely used in the healthcare sector, which aid in better decision-making for a radiologist. PET imaging or positron emission tomography is one of the most reliable approaches for a radiologist to di...
| Publicado en: | BioMed Research International pp. 1 - 18 |
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| Autores principales: | , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
1/10/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=154591979&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154591979 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 1/10/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 154591979 154591979 154591979 10.1155/2022/4185835 154591979 ppf: 1 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Cloud-Based Lung Tumor Detection and Stage Classification Using Deep Learning Techniques. aug: au: Kasinathan, Gopi Jayakumar, Selvakumar affil: Dept. of ECE, SRMIST, Chennai, India sug: subj: Cloud Computing Lung Neoplasms Diagnosis Diagnosis, Computer Assisted Neoplasm Staging Deep Learning Lung Neoplasms Classification Disease Progression Neural Networks (Computer) Positron-Emission Tomography Tomography, X-Ray Computed Human Benchmarking Sensitivity and Specificity Lung Neoplasms Radiography Radiography, Thoracic DICOM Comparative Studies Precision Descriptive Statistics Cancer Patients ab: Artificial intelligence (AI), Internet of Things (IoT), and the cloud computing have recently become widely used in the healthcare sector, which aid in better decision-making for a radiologist. PET imaging or positron emission tomography is one of the most reliable approaches for a radiologist to diagnosing many cancers, including lung tumor. In this work, we proposed stage classification of lung tumor which is a more challenging task in computer-aided diagnosis. As a result, a modified computer-aided diagnosis is being considered as a way to reduce the heavy workloads and second opinion to radiologists. In this paper, we present a strategy for classifying and validating different stages of lung tumor progression, as well as a deep neural model and data collection using cloud system for categorizing phases of pulmonary illness. The proposed system presents a Cloud-based Lung Tumor Detector and Stage Classifier (Cloud-LTDSC) as a hybrid technique for PET/CT images. The proposed Cloud-LTDSC initially developed the active contour model as lung tumor segmentation, and multilayer convolutional neural network (M-CNN) for classifying different stages of lung cancer has been modelled and validated with standard benchmark images. The performance of the presented technique is evaluated using a benchmark image LIDC-IDRI dataset of 50 low doses and also utilized the lung CT DICOM images. Compared with existing techniques in the literature, our proposed method achieved good result for the performance metrics accuracy, recall, and precision evaluated. Under numerous aspects, our proposed approach produces superior outcomes on all of the applied dataset images. Furthermore, the experimental result achieves an average lung tumor stage classification accuracy of 97%-99.1% and an average of 98.6% which is significantly higher than the other existing techniques. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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