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  Published Paper Details:

  Paper Title

A DEEP LEARNING SYSTEM POWERED BY IOT TO EARLY RECOGNISE AND CLASSIFY CERVICAL CELLS

  Authors

  Anju Lata Agnihotri,  Dr. Jitendra Sheethlani

  Keywords

Pre-trained, Transfer,IoHT, Regression, Classification.

  Abstract


Cervical disease is one of the quickest developing worldwide medical issues and drivingreason for mortality among women of agricultural nations. Mechanized Pap smear cell acknowledgment and classification in beginning phase of cell improvement is urgent for effective sickness determination and quick therapy. In this article, we proposed a clever Internet of HealthcareThings (IoHT) driven profound learning system for recognition and classification of cervical malignant growth in Pap smear pictures utilizing idea of move learning. Following exchange learning, Convolutional NeuralNetwork (CNN) was joined with different traditional AI procedures like K nearest neighbour, Guileless Bayes, calculated relapse, irregular woodland and backing vector machines. In the proposed system, highlight extraction from cervical pictures is performed utilizing pre-prepared CNN models like InceptionV3, VGG19, Squeeze Net furthermore, ResNet50, which are taken care of into thick and swelled layer for typical and abnormal cervical cells classification. The exhibition of the proposed IoHT systems is assessed utilizing standard Pap smear Helve dataset. The proposed approach was approved by breaking down accuracy, review, F1-score, preparing testing time and backing boundaries. The results reasoned that CNN pre-prepared model ResNet50 accomplished the higher classification pace of 97.89% with the contribution of irregular backwoods classifier for effective and dependable infection discovery and classification. The least preparation time and testing time expected to prepare model were 0.032 s and 0.006 s, separately.

  IJCRT's Publication Details

  Unique Identification Number - IJCRTN020018

  Paper ID - 219642

  Page Number(s) - 160-170

  Pubished in - Volume 8 | Issue 3 | March 2020

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Anju Lata Agnihotri,  Dr. Jitendra Sheethlani,   "A DEEP LEARNING SYSTEM POWERED BY IOT TO EARLY RECOGNISE AND CLASSIFY CERVICAL CELLS", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.8, Issue 3, pp.160-170, March 2020, Available at :http://www.ijcrt.org/papers/IJCRTN020018.pdf

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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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