| 000 -LEADER |
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| fixed length control field |
02120nam a22002657a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
20221105101943.0 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20250505111037.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
221105b ||||| |||| 00| 0 eng d |
| 041 ## - LANGUAGE CODE |
|---|
| Language code of text/sound track or separate title |
eng |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
616.240285 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Preferred name for the person |
Oraño, Jannie Fleur V. |
| Relator term |
author |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
CXR-based lung disease classification using convolutional neural network / |
| Statement of responsibility, etc |
Jannie Fleur V. Oraño |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
viii, 83 leaves: |
| Other physical details |
color illustrations; |
| Dimensions |
28 cm |
| 336 ## - CONTENT TYPE |
|---|
| Content type term |
text |
| Content type code |
txt |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
|---|
| Media type term |
unmediated |
| Media type code |
n |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
|---|
| Carrier type term |
volume |
| Carrier type code |
nc |
| Source |
rdacarrier |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Thesis (College of Computer Studies) -- Cebu Institute of Technology University |
| Year degree granted |
October 2019 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references. |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc |
Lung disease like effusion, pneumothorax, atelectasis and tuberculosis are some of the most severe and prevailing health problems in people’s life. A large percentage of the human population around the world is diagnosed annually with lung disease affecting adults, teens, children, smokers, and even non-smokers making it be considered as the leading cause of death and disability worldwide. Early and accurate diagnosis of chest diseases in mandatory and needed for timely and successful treatment, prevents further complications and a higher likelihood of survival. This study demonstrated the feasibility of classifying lung diseases in chest X-rays using conventional and deep learning approaches. With 8,125 sample images, the neural network model managed to achieve 82.53% accuracy in the classification. This accuracy rate indicates the generated model can significantly automate the differential diagnosis of lung disease using chest radiograph. The developed applications, when used as a second opinion, can help assists radiologist and doctors in their diagnosis and decision making. However, this accuracy can be further improved if more and balanced training images will be utilized. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Lungs |
| General subdivision |
Diseases. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Neural networks (Computer science) |
| 650 #2 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Diagnostic Imaging |
| General subdivision |
methods |
| 655 #0 - INDEX TERM--GENRE/FORM |
|---|
| Genre/form data or focus term |
Academic theses. |
| 942 ## - ADDED ENTRY ELEMENTS |
|---|
| Source of classification or shelving scheme |
|
| Item type |
THESIS / DISSERTATION |