Machine learning-based identification and classification of pulmonary nodules
Date of Publication
3-3-2023
Document Type
Bachelor's Thesis
Degree Name
Bachelor of Science in Computer Engineering
Subject Categories
Computer Engineering | Electrical and Computer Engineering
College
Gokongwei College of Engineering
Department/Unit
Electronics And Communications Engg
Thesis Advisor
John Anthony C. Jose
Defense Panel Chair
Armie E. Pakzad
Defense Panel Member
Carlo Noel E. Ochotorena
Lawrence Y. Materum
Abstract/Summary
The current clinical practice performed in the diagnosis of lung cancer is a thorough yet lengthy procedure featuring X-ray scans, CT scans, and depending on the initial findings - lung biopsies and other kinds of imaging tests. However, these tests tend to be highly invasive and costly. An alternative to this are computer-aided diagnosis tools which involve deep learning models that fully automate the prediction in lung diagnosis, reducing or potentially eliminating the invasiveness and costliness of procedures present in the current clinical practice. This study aimed to develop a medical imaging application that allows for automatic identification and classification of the pulmonary nodules present in chest CT scans utilizing a deep learning model and utilizing the Lung Image Database Consortium Image Collection (LIDC-IDRI) dataset and its subset, LUng Nodule Analysis 2016 (LUNA16). Furthermore, the study involved the implementation of two models - Nodule Detection and Segmentation Model and Nodule Classification Model. For the segmentation model, the NoduleNet framework was used which consisted of 3 parts: candidate generation, decoupled false positive reduction, and segmentation refinement. The researchers were able to achieve a detection performance score of 89.74% and a segmentation DICE of 72.89. As for the classification model, the model architecture was based on Inception v3 and used techniques such as test-time augmentation and random oversampling to achieve an accuracy of 76.97%. Both models were successfully created and were used in the medical imaging application to assist clinicians in giving diagnoses.
Abstract Format
html
Language
English
Format
Electronic
Keywords
Lungs—Cance—Diagnostic imaging; Machine learning
Recommended Citation
Coteok, T. C., Llarenas, L. S., Portugal, J. V., & Toro, M. S. (2023). Machine learning-based identification and classification of pulmonary nodules. Retrieved from https://animorepository.dlsu.edu.ph/etdb_ece/34
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Embargo Period
4-11-2023
