MARC details
| 000 -LEADER |
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| fixed length control field |
03406nam a22003497a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20250718125217.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
250718b |||||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
TUPM |
| Language of cataloging |
English |
| Transcribing agency |
TUPM |
| Modifying agency |
TUPM |
| Description conventions |
rda |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
BTH TK 870 |
| Item number |
A53 2025 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Anciano, Jowan Clyderie C. |
| Relator term |
author |
| 245 ## - TITLE STATEMENT |
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| Title |
Liverscan++: raspberry pi-based web-integrated device for detection and correlation of focal liver lesions from triphasic contrast-enhanced ct scans using deep learning algorithm/ |
| Statement of responsibility, etc. |
Jowan Clyderie C. Anciano, Chelsea R. Angeles, Gillian C. Gunita, Ann Jenette M. Gutierrez, Desiree Ann B. Nieto, and Mikko O. Reyes.-- |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Place of publication, distribution, etc. |
Manila: |
| Name of publisher, distributor, etc. |
Technological University of the Philippines, |
| Date of publication, distribution, etc. |
2025. |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
iii, 187pages: |
| Dimensions |
29cm. |
| 336 ## - CONTENT TYPE |
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| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
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| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
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| Source |
rdacarrier |
| 500 ## - GENERAL NOTE |
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| General note |
<br/><br/>Bachelor's thesis |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
College of Engineering.-- |
| Degree type |
Bachelor of science of electronics engineering: |
| Name of granting institution |
Technological University of the Philippines, |
| Year degree granted |
2025. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographic references and index. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
The detection of results involving liver diseases requires manual interpretation that<br/>often takes several days to weeks to be completed leading to delays that may result to late<br/>treatment options for the patients. The LIVERSCAN++ system, a Raspberry Pi-based,<br/>web-integrated device, was developed to detect and correlate focal liver lesions from<br/>triphasic contrast-enhanced computer tomography (CECT) images using a deep learning<br/>algorithm. The automated detection involves a pre-processing technique with the use of<br/>three phases of gray scaled CECT scans, namely unenhanced, arterial, and portal venous,<br/>by converting it into RGB format through assigning each phase to a specific channel and<br/>stacking it vertically. The stacked images underwent custom training using YOLOv11<br/>Instance Segmentation model to enable the system to identify specific liver lesions<br/>accurately and correlate each phase of the CECT scans’ phases. Furthermore, the device is<br/>paired with a graphical user interface that was designed solely for medical professionals’<br/>use. This interface was created for uploading images, accessing patients’ database, and<br/>making predictions based on the automated results. The device only acts as an aid for<br/>professionals to ease clinical workloads and improve radiological assessment. A website<br/>platform was integrated with the interface and was created for both medical professionals<br/>and their patients. Patients can access their verified results in real time. The model achieved<br/>strong performance with a mean Average Precision (mAP@50) of 0.902 and overall<br/>accuracy of 96.7%. Processing times averaged 6.5 seconds over Wi-Fi and 10.1 seconds<br/>on mobile data. Usability testing showed high effectiveness (score 4.90 on ISO 62366),<br/>while software quality evaluation (ISO 25010) indicated room for improvement, especially<br/>by expanding the training data and refining validation to boost accuracy and reliability. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Liver detection |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Liver detection |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Deep learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Medical imaging |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Angeles, Chelsea R. |
| Relator term |
author |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Gunita, Gillian C. |
| Relator term |
author |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Gutierrez, Ann Jenette M. |
| Relator term |
author |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Nieto, Desiree Ann B. |
| Relator term |
author |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Reyes, Mikko O. |
| Relator term |
author |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Bachelor's Thesis COE |
| Suppress in OPAC |
No |