Pre-trained convolutional neural networks in the assessment of bone scans for metastasis
- Author:
Vincent Peter C. Magboo
1
,
2
;
Ma. Sheila A. Magboo
1
Author Information
1. Department of Physical Sciences and Mathematics, University of the Philippines Manila
2. Section of Nuclear Medicine, University of Perpetual Help Medical Center
- Publication Type:Journal Article
- Keywords:
Convolutional Neural Networks;
Bone Metastasis
- MeSH:
Deep Learning;
Machine Learning
- From:
The Philippine Journal of Nuclear Medicine
2021;16(2):46-53
- CountryPhilippines
- Language:English
-
Abstract:
Background:Numerous applications of artificial intelligence have been applied in radiological imaging ranging from
computer-aided diagnosis based on machine learning to deep learning using convolutional neural networks.
One of the nuclear medicine imaging tests being commonly performed today is bone scan. The use of deep
learning methods through convolutional neural networks in bone scintigrams has not been fully explored. Very
few studies have been published on its diagnostic capability of convolutional neural networks in assessing
osseous metastasis.
Objective:The aim of our study is to assess the classification performance of the pre-trained convolutional neural
networks in the diagnosis of bone metastasis from whole body bone scintigrams of a local institutional dataset.
Methods:Bone scintigrams from all types of cancer were retrospectively reviewed during the period 2019-2020 at the
University of Perpetual Help Medical Center in Las Pinas City, Metro Manila. The study was approved by the
Institutional Ethical Review Board and Technical Review Board of the medical center. Bone scan studies should
be mainly for metastasis screening. The pre-processing techniques consisting of image normalization, image
augmentation, data shuffling, and train-test split (testing at 30% and the rest (70%) was split 85% for training
and 15% for validation) were applied to image dataset. Three pre-trained architectures (ResNet50, VGG19,
DenseNet121) were applied to the processed dataset. Performance metrics such as accuracy, recall (sensitivity),
precision (positive predictive value), and F1-scores were obtained.
Results:A total of 570 bone scan images with dimension 220 x 646 pixel sizes in .tif file format were included in this
study with 40% classified with bone metastasis while 60% were classified as without bone metastasis.
DenseNet121 yielded the highest performance metrics with an accuracy rate of 83%, 76% recall, 86% precision,
and 81% F1-score. ResNet50 and VGG19 had similar performance with each other across all metrics but
generally lower predictive capability as compared to DenseNet121.
Conclusion:A bone metastasis machine learning classification study using three pre-trained convolutional neural networks
was performed on a local medical center bone scan dataset via transfer learning. DenseNet121 generated the
highest performance metrics with 83% accuracy, 76% recall, 86% precision and 81% F1-score. Our simulation
experiments generated promising outcomes and potentially could lead to its deployment in the clinical practice
of nuclear medicine physicians. The use of deep learning techniques through convolutional neural networks has
the potential to improve diagnostic capability of nuclear medicine physicians using bone scans for the
assessment of metastasis.
- Full text:Pre-trained Convolutional.pdf
