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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
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| Paper Title: | Approach for classification of medical image using deep learning technique |
| Authors Name: | Shweta Prafull Ajmera , Prof. Sarika B. Solanke |
| Unique Id: | IJSDR1911019 |
| Published In: | Volume 4 Issue 11, November-2019 |
| Abstract: | This paper tends to the issue of fragmenting an image into the segment. The magnetic resonance imaging (MRI) process is susceptible to a wide range of artifacts caused by various sources. In some cases, artifacts might be confused with pathology. In addition, state-of-the-art dynamic MR reconstruction algorithms are iterative in nature, causing longer reconstruction times. Recently, deep learning has been applied to MRI reconstruction and produces high quality images at high acceleration rates. Since deep learning highly depends on training data, the quality of training images must not be ignored. This article demonstrates how noisy images in the training data affect the quality of MR reconstruction. The proposed method modifies the loss function of the neural network to prefer higher quality target images by using a weighted loss function. In this paper mean squared error loss is used, but the approach can be extended to other types of loss function. Using still frames from cardiac MRI’s, this approach is compared to existing approaches that discard noisy training data or ignore these quality differences. Even a basic weighting strategy improves the deep learning reconstruction quality over such methods. Our purpose is to develop a mammography-based DL breast cancer risk model that is more accurate than established clinical breast cancer risk models |
| Keywords: | mass segementation, deep learning |
| Cite Article: | "Approach for classification of medical image using deep learning technique", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.4, Issue 11, page no.111 - 116, November-2019, Available :http://www.ijsdr.org/papers/IJSDR1911019.pdf |
| Downloads: | 000337067 |
| Publication Details: | Published Paper ID: IJSDR1911019 Registration ID:191118 Published In: Volume 4 Issue 11, November-2019 DOI (Digital Object Identifier): Page No: 111 - 116 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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