Citation: Chen, Y.-T.; Chen, Y.-L.;
Chen, Y.-Y.; Huang, Y.-T.; Wong, H.-F.;
Yan, J.-L.; Wang, J.-J. Deep
Learning–Based Brain Computed
Tomography Image Classification
with Hyperparameter Optimization
through Transfer Learning for Stroke.
Diagnostics 2022, 12, 807. https://
doi.org/10.3390/diagnostics12040807
Academic Editors: Keun Ho Ryu and
Nipon Theera-Umpon
Received: 11 February 2022
Accepted: 24 March 2022
Published: 25 March 2022
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Article
Deep Learning–Based Brain Computed Tomography Image
Classification with Hyperparameter Optimization through
Transfer Learning for Stroke
Yung-Ting Chen
1
, Yao-Liang Chen
1,
*, Yi-Yun Chen
1
, Yu-Ting Huang
1
, Ho-Fai Wong
2
, Jiun-Lin Yan
3
and Jiun-Jie Wang
1
1
Department of Diagnostic Radiology, Chang Gung Memorial Hospital, Keelung 204201, Taiwan;
yungting12@cgmh.org.tw (Y.-T.C.); rsc8418@cgmh.org.tw (Y.-Y.C.); m7131@cgmh.org.tw (Y.-T.H.);
jwang@mail.cgu.edu.tw (J.-J.W.)
2
Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Chang Gung University,
Linkou 333423, Taiwan; hfwong@cgmh.org.tw
3
Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung 204201, Taiwan;
colorgenie@cgmh.org.tw
* Correspondence: chenyl0702@cgmh.org.tw; Tel.: +886-2-24313131
Abstract:
Brain computed tomography (CT) is commonly used for evaluating the cerebral condition,
but immediately and accurately interpreting emergent brain CT images is tedious, even for skilled
neuroradiologists. Deep learning networks are commonly employed for medical image analysis
because they enable efficient computer-aided diagnosis. This study proposed the use of convolutional
neural network (CNN)-based deep learning models for efficient classification of strokes based on
unenhanced brain CT image findings into normal, hemorrhage, infarction, and other categories. The
included CNN models were CNN-2, VGG-16, and ResNet-50, all of which were pretrained through
transfer learning with various data sizes, mini-batch sizes, and optimizers. Their performance in
classifying unenhanced brain CT images was tested thereafter. This performance was then compared
with the outcomes in other studies on deep learning–based hemorrhagic or ischemic stroke diagnoses.
The results revealed that among our CNN-2, VGG-16, and ResNet-50 analyzed by considering several
hyperparameters and environments, the CNN-2 and ResNet-50 outperformed the VGG-16, with an
accuracy of 0.9872; however, ResNet-50 required a longer time to present the outcome than did the
other networks. Moreover, our models performed much better than those reported previously. In
conclusion, after appropriate hyperparameter optimization, our deep learning–based models can
be applied to clinical scenarios where neurologist or radiologist may need to verify whether their
patients have a hemorrhage stroke, an infarction, and any other symptom.
Keywords: machine learning; neuroradiology; computed tomography; stroke; classification
1. Introduction
Brain computed tomography (CT) is a modality most commonly used for evaluating
the cerebral condition [
1
]. It is more widely available, fast, and cost-effective than is
brain magnetic resonance imaging. Although brain CT was developed in the 1970s, its
widespread clinical use became achievable only recently, after the introduction of rapid,
large-coverage multidetector-row CT scanners. Key clinical applications for brain CT
include the diagnoses of cerebral hemorrhage and ischemia neoplasm and evaluation of
the mass effect after hemorrhage, neoplasm, and cerebral edema secondary to ischemia.
However, immediate and highly accurate interpretation of emergent CT images remains
time-consuming and laborious, even for skilled neuroradiologists [
2
]. Lodwick described
computer-aided diagnosis (CAD) for the first time. Since then, a wide variety of lesion
detection systems have been reported [
3
,
4
]. The usefulness of CAD depends on the number
Diagnostics 2022, 12, 807. https://doi.org/10.3390/diagnostics12040807 https://www.mdpi.com/journal/diagnostics
