Please use this identifier to cite or link to this item: http://drsr.daiict.ac.in//handle/123456789/687
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dc.contributor.advisorJoshi, Manjunath V.
dc.contributor.authorSharma, Priyanka
dc.date.accessioned2018-05-17T09:29:56Z
dc.date.available2018-05-17T09:29:56Z
dc.date.issued2017
dc.identifier.citationPriyanka Sharma(2017).MEDICAL IMAGING USING MACHINE LEARNING TECHNIQUES.Dhirubhai Ambani Institute of Information and Communication Technology.viii, 45 p.(Acc.No: T00651)
dc.identifier.urihttp://drsr.daiict.ac.in//handle/123456789/687
dc.description.abstract"We present an application of deep Convolutional Neural Networks (CNN) for theclassification of mammogram as normal, benign and malignant tumor. The images used in this study have been used from the mini-MIAS dataset of mammograms. The proposed system has been implemented in three stages: (a) preprocessing of the dataset which includes resizing and rotation of the original mammogram (b) feature extraction by using a CNN autoencoder (c) classification task using the previously extracted features by training of a deep CNN classifier. In this research, the goal of the system is to distinguish between three classes of mammograms: benign, malignant or without tumor. In this thesis, we implemented an approach using self-learned features which are extracted from the utoencoder. These features are provided to a CNN classifier for classification. Our approach uses deep CNN for feature extraction and classification task. This approach is compared with the methods in which hand crafted features and different classifiers are used. Our approach provides better results than these methods. Hence, our approach do not require any feature engineering."
dc.publisherDhirubhai Ambani Institute of Information and Communication Technology
dc.subjectComputer aided diagnosis
dc.subjectTumor
dc.subjectNeoplasm
dc.subjectArtificial Neural network
dc.classification.ddc616.0754 SHA
dc.titleMEDICAL IMAGING USING MACHINE LEARNING TECHNIQUES
dc.typeDissertation
dc.degreeM.Tech.
dc.student.id201511035
dc.accession.numberT00651
Appears in Collections:M Tech Dissertations

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