Early detection of covid-19 using machine learning (Record no. 17258)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | a |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220805141822.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220805b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 17465 |
| Author | Singh, Tismeet |
| 245 ## - TITLE STATEMENT | |
| Title | Early detection of covid-19 using machine learning |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.7 (01), Jan-Feb |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | New Delhi |
| Year | 2022 |
| Name of publisher, distributor, etc. | Associated Management Consultants |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 8-24p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | The COVID-19 Pandemic had a devastating impact both on social and economic fronts for a majority of the countries around the world. It spread at an exponential rate and affected millions of people across the globe. The aim of this study was to improve upon a lot of existing studies on COVID detection using Machine Learning. While Machine Learning methods have been widely used in other medical domains, there is now considerable demand for ML-guided diagnostic systems for screening, tracking, analysing, and predicting the spread of COVID-19 and finding a concrete and viable cure for it. We employed the power of Transfer Learning guided Convolutional Networks to predict the existence of the COVID-19 virus in the lung X-Ray of any subject. Deep Learning, one of the most lucrative and potent techniques of machine learning becomes the modern saviour when such crises arise. With the power of this technique, we studied a plethora of models, selected the best ones and then trained them to produce the most optimal results. We used multiple pretrained models and improved upon them by adding structured Dense and Batch Normalisation layers with appropriately selecting activation functions. Elaborate testing yielded a maximum accuracy of over 99%. |
| Expansion of summary note | <br/>Keywords<br/><br/>Computer Vision, Confusion Matrix, Convolutional Neural Network, COVID-19, Deep Learning, Machine Learning, Transfer Learning, X-Ray. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4622 |
| Topical term or geographic name entry element | Computer Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 17466 |
| Co-Author | Agarwal, Kartikeya |
| 773 0# - HOST ITEM ENTRY | |
| International Standard Serial Number | 2456-4133 |
| Title | Indian Journal of Computer Science |
| Place, publisher, and date of publication | New Delhi Associated Management Consultants |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | Reference | School of Engineering & Technology | School of Engineering & Technology | Archieval Section | 05/08/2022 | 2022-1279 | 05/08/2022 | 05/08/2022 | Articles Abstract Database |