Prohealth suite: Ptb portal with yolo11 capabilities/ John Gerald M. Hipulan, Davis Louis A. Icaro, Kyle Gabriel Kristofferson S. Maraya, Aaron Paul M. Sicio, and Paolo Miguel L. Sunga.--

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Hipulan, John Gerald M [author]

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Material type: Text

TextPublication details: Manila: Technological University of the Philippines, 2025.Description: ix, 77pages: 29cmContent type:

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BTH QA 76 H57 2025

Dissertation note: College of Science.-- Bachelor of science in computer science: Technological University of the Philippines, 2025. Summary: Early detection and diagnosis of diseases are critical to improving patient outcomes and reducing mortality rates. This thesis presents the ProHealth Suite, a diagnostic tool that leverages YOLOv11 to detect pleural effusion and cavities—conditions that may indicate probable pulmonary tuberculosis—from medical images, using chest X-rays as the primary input. This research demonstrates the potential of YOLOv11 in automating and improving the accuracy of disease detection, potentially enhancing the early diagnosis process in clinical settings. The ProHealth Suite represents a significant step towards the development of comprehensive, AI-powered diagnostic tools that can be widely implemented in healthcare systems worldwide.

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Item type	Current library	Shelving location	Call number	Copy number	Status	Date due	Barcode
Bachelor's Thesis COS	TUP Manila Library	Thesis Section-2nd floor	BTH QA 76 H57 2025 (Browse shelf(Opens below))	c.1.	Not for loan		BTH0006612

Bachelor's thesis

College of Science.-- Bachelor of science in computer science: Technological University of the Philippines, 2025.

Includes bibliographic references and index.

Early detection and diagnosis of diseases are critical to improving patient outcomes and
reducing mortality rates. This thesis presents the ProHealth Suite, a diagnostic tool that leverages
YOLOv11 to detect pleural effusion and cavities—conditions that may indicate probable
pulmonary tuberculosis—from medical images, using chest X-rays as the primary input.

This research demonstrates the potential of YOLOv11 in automating and improving the
accuracy of disease detection, potentially enhancing the early diagnosis process in clinical
settings. The ProHealth Suite represents a significant step towards the development of
comprehensive, AI-powered diagnostic tools that can be widely implemented in healthcare
systems worldwide.

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