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Medical Report Generation Model

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dc.contributor.advisor Kulkarni, Viraj en_US
dc.contributor.author GUPTA, PAWAN en_US
dc.date.accessioned 2021-07-13T05:48:36Z
dc.date.available 2021-07-13T05:48:36Z
dc.date.issued 2021-07
dc.identifier.citation 42 en_US
dc.identifier.uri http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/6075
dc.description.abstract After recovery, many Covid Patients have a post-corona effect, and the most common is a lung infection (Pneumonia) in treatment response of this the first things that are done are taking X-rays and creating Reports. Medical images like X-rays, CT scans, and MRIs possess a lot of information in them, like the possible presence of some pathologies or the changes in the normal functioning of the organs. The information present in this data get transcribed by Radiologists in plain text reports, which summarise the crucial findings from the images. We are trying to create the medical report generation using artificial intelligence, more precisely using LSTM and Convolutional Neural Network. We want to propose the best method for generating text medical reports by comparing the hierarchical encoder-decoder network with and without attention mechanism. We have approached Models computationally and doesn’t get into depth of Mathematics. en_US
dc.language.iso en en_US
dc.subject Medical Report Generation Model en_US
dc.title Medical Report Generation Model en_US
dc.type Thesis en_US
dc.type.degree BS-MS en_US
dc.contributor.department Dept. of Data Science en_US
dc.contributor.registration 20161038 en_US


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  • MS THESES [1705]
    Thesis submitted to IISER Pune in partial fulfilment of the requirements for the BS-MS Dual Degree Programme/MSc. Programme/MS-Exit Programme

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