Smart IoT in Breast Cancer Detection Using Optimal Deep Learning.
IoT in healthcare systems is currently a viable option for providing higher-quality medical care for contemporary e-healthcare. Using an Internet of Things (IoT)–based smart healthcare system, a trustworthy breast cancer classification method called Feedback Artificial Crow Search (FACS)–based Sheph...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 4; pp. 1489 - 1507 |
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| Autores principales: | , , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2023
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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=169808827&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 169808827 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2023 vid: 36 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 169808827 163864485 169808827 169808827 10.1007/s10278-023-00834-9 169808827 ppf: 1489 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Smart IoT in Breast Cancer Detection Using Optimal Deep Learning. aug: au: Majji, Ramachandro G., Om Prakash P. Rajeswari, R. R., Cristin affil: Department of Information Technology, Vardhaman College of Engineering, Kacharam, Hyderabad, Telangana, India sug: subj: Internet of Things Breast Neoplasms Diagnosis Deep Learning Cancer Screening Human Mammography Breast Neoplasms Classification Sensitivity and Specificity Descriptive Statistics Neural Networks (Computer) Algorithms Data Management Image Enhancement ab: IoT in healthcare systems is currently a viable option for providing higher-quality medical care for contemporary e-healthcare. Using an Internet of Things (IoT)–based smart healthcare system, a trustworthy breast cancer classification method called Feedback Artificial Crow Search (FACS)–based Shepherd Convolutional Neural Network (ShCNN) is developed in this research. To choose the best routes, the secure routing operation is first carried out using the recommended FACS while taking fitness measures such as distance, energy, link quality, and latency into account. Then, by merging the Crow Search Algorithm (CSA) and Feedback Artificial Tree, the produced FACS is put into practice (FAT). After the completion of routing phase, the breast cancer categorization process is started at the base station. The feature extraction step is then introduced to the pre-processed input mammography image. As a result, it is possible to successfully get features including area, mean, variance, energy, contrast, correlation, skewness, homogeneity, Gray Level Co-occurrence Matrix (GLCM), and Local Gabor Binary Pattern (LGBP). The quality of the image is next enhanced through data augmentation, and finally, the developed FACS algorithm's ShCNN is used to classify breast cancer. The performance of FACS-based ShCNN is examined using six metrics, including energy, delay, accuracy, sensitivity, specificity, and True Positive Rate (TPR), with the maximum energy of 0.562 J, the least delay of 0.452 s, the highest accuracy of 91.56%, the higher sensitivity of 96.10%, the highest specificity of 91.80%, and the maximum TPR of 99.45%. 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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