CNN BASED SKIN CANCER CELL DETECTION SYSTEM FROM DERMOSCOPIC IMAGES.
In these days, the use of machine learning and internet has occupied a large space in the field of Bio medical Technology. The machine learning is capable of make decisions based on the past data and it will used for many disease identification and treatment. It has changed our way of health monitor...
| Publicado en: | Turkish Journal of Physiotherapy Rehabilitation Vol. 32; no. 2; pp. 1452 - 1460 |
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| Autores principales: | , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Turkish Journal of Physiotherapy & Rehabilitation
2021
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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=151006122&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151006122 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13008757 YU1 jtl: Turkish Journal of Physiotherapy Rehabilitation issn: 13008757 maglogo: N pubinfo: dt: 2021 vid: 32 iid: 2 pid: 20392 pub: Turkish Journal of Physiotherapy & Rehabilitation place: Kizilay/ Ankara, <Blank> artinfo: ui: 151006122 151006122 151006122 151006122 ppf: 1452 ppct: 8 formats: fmt: @attributes: type: P tig: atl: CNN BASED SKIN CANCER CELL DETECTION SYSTEM FROM DERMOSCOPIC IMAGES. aug: au: SIVASANKARI, K. AKASH, C. J. GANESH, M. DILLI SIJIN, R. ARIKNESH affil: Associate Professor, Department of Electronics and Communication, Sri Sairam Institute of Technology, Chennai, Tamil Nadu, India sug: subj: Neural Networks (Computer) Skin Neoplasms Diagnosis Microscopy Methods Machine Learning Image Interpretation, Computer Assisted Human Deep Learning Internet of Things Image Processing, Computer Assisted Data Analysis Software Software Descriptive Statistics Dermatologists ab: In these days, the use of machine learning and internet has occupied a large space in the field of Bio medical Technology. The machine learning is capable of make decisions based on the past data and it will used for many disease identification and treatment. It has changed our way of health monitoring, diagnosis the disease and its level. In this proposal we use the Machine learning to acquire data from dermoscopy image and from that we diagnosis the impact of disease and monitor it on realtime process. Deep learning methods such as deep convolutional neural networks (CNNs) have established an overwhelming presence in image recognition tasks in the past few years. The main advantage of CNN is that it is endowed with an impressive visual representation capability for the recognition or detection task depending on the given training dataset. We present our proposed framework in details. Here the deep residual neural network is applied in our method, followed by the extraction of local dense activations as deep convolutional features in our framework. Then, FV (fisher vector) encoding strategy is utilized to aggregate these deep features for more discriminative and robust representations. Finally, the classification method of the FV representations is present. Then the result is updated to a webserver by using IOT. Based on the data received from the MATLAB we will indicate the types of disease. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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