Deep Learning-based Anomaly Detection on X-Ray Images of Fuel Cell
Electrodes
Simon B. Jensen
1 a
, Thomas B. Moeslund
1 b
and Søren J. Andreasen
2
1
Department of Architecture and Media Technology, Aalborg University, Aalborg, Denmark
2
Serenergy, Aalborg, Denmark
Keywords:
Anomaly Detection, Deep Learning, Convolutional Neural Network, X-Ray, Data Augmentation, Transfer
Learning, Quality Control.
Abstract:
Anomaly detection in X-ray images has been an active and lasting research area in the last decades, especially
in the domain of medical X-ray images. For this work, we created a real-world labeled anomaly dataset,
consisting of 16-bit X-ray image data of fuel cell electrodes coated with a platinum catalyst solution and
perform anomaly detection on the dataset using a deep learning approach. The dataset contains a diverse
set of anomalies with 11 identified common anomalies where the electrodes contain e.g., scratches, bubbles,
smudges etc. We experiment with 16-bit image to 8-bit image conversion methods to utilize pre-trained
Convolutional Neural Networks as feature extractors (transfer learning) and find that we achieve the best
performance by maximizing the contrasts globally across the dataset during the 16-bit to 8-bit conversion,
through histogram equalization. We group the fuel cell electrodes with anomalies into a single class called
abnormal and the normal fuel cell electrodes into a class called normal, thereby abstracting the anomaly
detection problem into a binary classification problem. We achieve a balanced accuracy of 85.18%. The
anomaly detection is used by the company, Serenergy, for optimizing the time spend on the quality control of
the fuel cell electrodes.
1 INTRODUCTION
Serenergy is a world-leading supplier of methanol-
based fuel cell solutions with more than a thousand
active units deployed globally. The fuel cells provide
back-up power as well as temporary primary power or
work in a hybrid system with renewable sources such
as solar and/or wind. The core component of the fuel
cell system, Serenergy, is a cell stack of 120 high tem-
perature, polymer electrolyte membranes. Each cell
contains 2 fuel cell electrodes that are coated with a
platinum-based catalyst. Meaning the fuel cell sys-
tem is made up of 120 × 2 = 240 fuel cell electrodes
in total. Examples of fuel cell electrodes can be seen
in figure 1. We will use the term fuel cell electrode or
electrode interchangeably.
The quality of the platinum-based catalyst and the
quality of how well it is coated on to each electrode
is paramount to the overall conductivity of the fuel
cell. The quality of an electrode is measured through
a semi-automatic/manual process where an X-ray im-
a
https://orcid.org/0000-0002-3217-1360
b
https://orcid.org/0000-0001-7584-5209
age is manually captured of each electrode and the
X-ray image is analyzed by an image analysis tool
which outputs several quality parameters e.g., color
histograms, box plots, standard deviation of the colors
of the platinum coating, the minimum- and maximum
colors of the platinum coating etc.
Figure 1: Fuel cell electrodes coated with a platinum cata-
lyst.
The process of analyzing the output of the image
analysis tool is time-consuming and, in most cases,
the electrodes have an acceptable quality. Seren-
ergy wishes to optimize the quality control process
by using a deep learning approach to perform auto-
matic anomaly detection on the fuel cell electrodes,
by grouping them into two classes, normal or abnor-
mal. Where a normal electrode can be used in the final
Jensen, S., Moeslund, T. and Andreasen, S.
Deep Learning-based Anomaly Detection on X-Ray Images of Fuel Cell Electrodes.
DOI: 10.5220/0010785400003124
In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP, pages
323-330
ISBN: 978-989-758-555-5; ISSN: 2184-4321
Copyright
c
2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
323
fuel cell system and an abnormal electrode cannot.
In figure 2 examples of X-ray images of normal
and abnormal electrodes are shown. This paper de-
scribes our approach to this important problem and
the contributions are:
• 16-bit to 8-bit conversion methods for X-ray im-
ages of fuel cell electrodes using histogram equal-
ization.
• A Deep Convolutional Neural Network classifier
to perform anomaly detection of X-ray images of
fuel cell electrodes.
1.1 Related Work
1.1.1 Deep Learning-based Classification on
X-Ray Image
Deep learning is a widely used technology for clas-
sification of images. More specifically Deep Convo-
lutional Neural Networks (CNNs). The technological
development of CNNs has been accelerated by image
classification datasets and challenges such as pascal
VOC (Everingham et al., 2010) and ImageNet (Rus-
sakovsky et al., 2014).
For X-ray images, there is a lack of large publicly
available dataset, which means that few studies exists
using CNNs for classification in X-ray images.
However, in the specific domain of pneumonia de-
tection in chest X-ray images, a number of dataset
has recently been made publicly available for exam-
ple (Mooney, 2018), (Wang et al., 2017) and (Cohen
et al., 2020). This has resulted in a large number of
studies which use CNNs to classify X-ray images in
this specific field. (C¸ allı et al., 2021) reviews and
compares a large collection of these studies.
(Rahman et al., 2020) trains four well-recognized
CNN models pre-trained on the ImageNet dataset,
AlexNet (Krizhevsky et al., 2012), ResNet-18 (He
et al., 2015), DenseNet201 (Wang and Zhang, 2020)
and SqueezeNet (Iandola et al., 2016) for detecting
pneumonia in the Pneumonia Chest X-ray dataset and
compare their performance. Similarly, (Jiang, 2020)
trains a CNN on the Pneumonia Chest X-ray dataset
to detect pneumonia while utilizing the Dynamic His-
togram Enhancement algorithm (Abdullah-Al-Wadud
et al., 2007) as pre-processing method to improve the
quality of X-ray images before training and evaluat-
ing the CNN model.
Other domains where deep learning-based clas-
sification has been applied to X-ray image data are
e.g., classification of threat objects in X-ray security
imaging for baggage inspection (Akcay and Breckon,
2021) and classification of dental caries in bitewing
X-ray images (Lee et al., 2021).
1.1.2 Transfer Learning for Image Classification
(Hussain et al., 2019) shows that transfer learning for
image classification using deep CNNs is a valid and
efficient method for achieving high performance in
image classification tasks when dealing with datasets
of limited size. They use the Inception-v3 (Szegedy
et al., 2015) CNN pre-trained on the ImageNet dataset
and re-train the model on the Caltech Face dataset
consisting of only 450 images while achieving an ac-
curacy of 65.7%.
(Rahman et al., 2020) validates transfer learning
for image classification when the base dataset (Ima-
geNet) consist of 3-channel RGB images and the tar-
get dataset consists of 1-channel grayscale X-Ray im-
ages. They do this by fine-tuning CNN models pre-
trained with ImageNet on chest X-ray datasets.
Several transfer learning methods have recently
been publicized which further optimizing the perfor-
mance which can be achieved by the method. (Wang
et al., 2019) proposes a method called attentive fea-
ture distillation and selection (AFDS), which adjusts
the strength of transfer learning regularization and
also dynamically determines the important features to
transfer. They impose the method onto ResNet-101
and achieve state-of-the art computation reduction.
1.1.3 Anomaly Detection using Deep Learning
(Alloqmani et al., 2021) reviews twenty studies which
utilizes deep learning for anomaly detection and iden-
tify challenges and insights in the domain. They iden-
tify the three main challenges for anomaly detection
to be: (1) handling the class imbalance of normal and
abnormal data, (2) the availability of labeled data and
(3) the fact that there is often noise in the data that
appears to be close to the actual anomalies and thus it
becomes difficult to differentiate them.
(Ho and Wookey, 2020) proposes a solution to
the class imbalance challenge, by introducing a new
loss function for binary- and multiclass classification
problems called Real-World-Weight Cross-Entropy
loss function. Which allows direct input of real
world costs as weights. This could prove useful for
classification problems where there is a well-defined
loss/cost for misclassified samples.
(Elgendi et al., 2021) proposes a solution to the
second challenge by introducing and comparing four
data augmentation methods for artificially increas-
ing the number of training samples of X-ray images,
while performing Covid-19 pneumonia detection us-
ing a CNN. The methods use combinations of random
rotations, shear, translation, horizontal- and vertical
flipping among other data augmentation methods.
VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications
324
(a) (b) (c) (d) (e)
Figure 2: Examples of X-ray images of fuel cell electrodes. Figure 2a, 2b and 2c are examples of abnormal plates, while
figure 2d and 2e are examples of normal plates. 2a is abnormal due to issues with scratches near its edges. 2b is abnormal due
to issues with lines and bubbles and 2c is abnormal due to issues with smudges.
We discuss the three challenges and possible solu-
tions further, in regards to anomaly detection in X-ray
images of fuel cell electrodes in section 4.
2 APPROACH
2.1 Overview
An overview of the anomaly detection approach pro-
posed in this paper is seen in figure 3. The approach
is described by the following three steps:
1. Convert the X-ray images from 16-bit to 8-bit, as
described in section 2.3.
2. Extract features using a pre-trained ResNet-34
CNN model as described in 2.4.
3. Classify anomalies in the feature-maps generated
by the CNN model, using fully connected layers,
as described in 2.4.1.
2.2 The Fuel Cell Electrode X-Ray
Dataset
A real-world labeled anomaly dataset, consisting of
16-bit X-ray images of platinum catalyst coated fuel
cell electrodes, is created for this work, as no exist-
ing dataset exists to the best of our knowledge, in this
specific domain.
The fuel cell electrode X-ray dataset consists of
714 X-ray images. Each electrode belongs to 1 of
12 bundles, named bundle 1, bundle 2, ..., bundle 12.
Where a bundle represents a collection of electrodes
which are coated with a platinum catalyst solution us-
ing the same coating method and mixture. The bun-
dles in the dataset have varying number of X-ray im-
ages. The class balance varies for each bundle as well.
The dataset is imbalanced with an over representa-
tion of normal samples. Across all 714 images 562
(78.71%) are labeled as normal and 152 (21.29%) are
labeled as abnormal.
A dataset size of 714 X-ray images is considered
to be a small dataset when utilizing a deep learning
approach. We utilize transfer-learning to overcome
this problem, using a pre-trained ResNet-34 model,
as described in section 2.4. Due to the limited size
of the dataset, creating a representative test set for the
dataset proves difficult. We evaluated the X-ray im-
ages of each bundle through cross-validation. This
is done by evaluating each bundle individually, while
the remaining bundles are used as training samples
and combining the results of each evaluation into a
total score. This is further described in section 3.
2.2.1 Fuel Cell Electrode X-Ray Images
The X-ray images of the dataset have varying dimen-
sions with a minimum width of 1119 pixels, maxi-
mum width of 1219 pixels and mean width of 1145
pixels. The minimum height of an electrode is 2053
pixels, the maximum height is 2115 pixels and the
mean 2072,1 pixels. During training and evaluation
of the anomaly detector, the X-ray images are trans-
formed into a uniform size of 2000 ×1000. Examples
of normal and abnormal electrode X-ray images can
be seen in figure 2.
2.2.2 Anomalies
Serenergy has identified 11 common anomaly types,
which are grouped into a single class called abnormal.
The identified 11 common anomaly types are named:
scratches, lines, edge cuts, edge tensions, smudges,
edge ink flow, bubbles, missing ink, agglomerate, ink
fluctuations, ink entry/exit.
Deep Learning-based Anomaly Detection on X-Ray Images of Fuel Cell Electrodes
325
Figure 3: An overview of the anomaly detection approach proposed in this paper made up by three main steps which are
described in section 2. Some of the layers of the ResNet-34 feature extractor are hidden for illustration purposes.
The normal fuel cell electrodes can have minor
representations of one or more anomaly type, as long
as the severity is not too great. Figure 2d is an exam-
ple of an electrode which have minor scratches, but
the scratches are not sever enough to be classified as
abnormal, while figure 2a is an example of a fuel cell
electrode which is abnormal due to scratches near its
edges.
2.3 X-Ray Image Conversion Methods
To utilize pre-trained CNNs such as ResNet-34 as fea-
ture extractor in the anomaly detector, the depth of the
electrode X-ray images is extended from 1 channel to
3 channels and the pixel values of the images are con-
verted from a 16-bit values into 8-bit values. Thous,
increasing the similarity of the electrode X-ray im-
ages to the images of ImageNet on which the CNN
is pre-trained on. This is further described in section
2.4.
The 16-bit color range consists of 2 to the power
of 16 (65536) colors and the 8-bit color range consists
of 2 to the power of 8 (256) colors. A loss of infor-
mation during the conversion is therefore inevitable.
We experiment with four methods for convert-
ing the X-ray images from 16- to 8-bits using his-
togram equalization and implemented using Python’s
OpenCV library (Itseez, 2015).
In section 2.3.1 a naive 16-bit to 8-bit conversion
method is described which is used as baseline. In sec-
tion 2.3.2 we use histogram equalization with global
maximum and minimum bounds calculated across
the entire dataset and in section 2.3.3 we use his-
togram equalization with local maximum and mini-
mum bounds calculated for each individual X-ray im-
age. Finally, we mix the methods in section 2.3.4.
Examples of the resulting 8-bit images, for each con-
version, are shown in figure 4.
2.3.1 Method 1: Naive Conversion
Method 1 is a naive conversion which is used as base-
line. Method 1 simply scales each 16-bit pixel value
and convert it to an unsigned 8-bit type.
An example of a resulting fuel cell electrode im-
age after conversion can be seen in figure 4a.
Most pixels lie in the range 1700-2800 in the 16-
bit X-ray images, as shown in section 2.3.2, which
means method 1 will appear very dark. Pixel values
in the 16-bit color range of 1700-2800 corresponds to
pixel values of 6-11 in the 8-bit range using naive
conversion.
Finally, the resulting 1-channel 8-bit image is ex-
tended with 2 additional channels, resulting in a 3.
channel 8-bit image.
2.3.2 Method 2: Conversion by Global Min and
Max
For method 2 the global maximum- and minimum
pixel value, G
max
and G
min
, of the 16-bit X-ray dataset
is calculated and used as upper- and lower-bounds for
histogram equalization, during 16-bit to 8-bit conver-
sion.
The global maximum pixel value is found by cal-
culating the 99.99th percentile of the pixel values in
all X-ray images in the dataset and taking the maxi-
mum pixel value found and round it to nearest hun-
dred. Similarly, we calculate G
min
by finding the
0.01th percentile. G
max
and G
min
are found to be
28000 and 1700. Method 2 is given by equation 1
and illustrated in figure 5.
f
2
(x
i
) = usign
max(min(x
i
, G
max
), G
min
)
G
max
− G
min
× 256
(1)
The reason for calculating the 99.99th and 0.01th
percentiles of the pixel values is to avoid that noise in
the X-ray images will affect the values of G
max
and
G
min
.
2.3.3 Method 3: Conversion by Local Min and
Max
Method 3 uses the local maximum- and minimum
pixel value, L
max
and L
min
, found for each individ-
VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications
326
(a) (b) (c) (d)
Figure 4: Example of the 8-bit images obtained by applying conversion method 1, 2, 3 and 4 to a 16-bit X-ray image. Figure
4a, 4b, 4c and 4d shows method 1, 2, 3 and 4 respectively.
Figure 5: Method 2 uses the global minimum- and max-
imum pixel values, G
max
and G
min
, calculated across the
entire fuel cell electrode X-ray dataset as upper- and lower
bounds to convert from 16-bit to 8-bit through histogram
equalization.
ual X-ray image, as upper- and lower-bound for his-
togram equalization.
Thereby, maximizing the contrast in the 8-bit
color range for each individual X-ray image. Simi-
larly to method 2, the 99.99th and 0.01th percentiles
of the pixel values for each X-ray image are used to
avoid that noise will affect the values of L
max
and L
min
.
The danger however, can be that the converted
pixel values in one X-ray image lose their meaning
relative to the converted pixel values of another X-ray
image. Consider an X-ray image, image
i
with a L
min
value of 2200 and another X-ray image, image
j
with
an L
min
value of 1800. After conversion, a value of
0 in the 8-bit color range will have corresponded to
2200 in image
i
and to 1800 in image
j
. Method 3 is
given by equation 2.
f
3
(k, x
i
) = usign
max(min(x
i
, L
k,max
), L
k,min
)
L
k,max
− L
k,min
× 256
(2)
Where L
k,max
and L
k,min
correspond to the local
maximum and minimum of the k’th image in the elec-
trode dataset.
2.3.4 Method 4: Mixing the Methods
Finally, method 4 mixes the conversion methods from
method 1, 2 and 3, such that the resulting image will
contain the 8-bit pixel values from method 1 in its
1. channel, the pixel values from method 2 in its 2.
channel and the pixel values from method 3 in its 3.
channel as seen in figure 6.
Figure 6: Method 4 mixes the conversion methods from
method 1, 2, 3 into a 3-channel image, with 1 channel cor-
responding to the values achieved by each of the methods.
2.4 Anomaly Detector Architecture
This paper proposes a fuel cell electrode anomaly de-
tector which uses a Convolutional Neural Network as
feature extractor. For this purpose, a PyTorch (Paszke
et al., 2019) implementation of the ResNet-34 (He
et al., 2015) CNN model is utilized.
The ResNet-34 model is pre-trained on a base
dataset (ImageNet (Russakovsky et al., 2014)). The
reason for using a pre-trained CNN, also referred to
as transfer learning, is that training CNNs from ran-
dom initializations, usually requires a large amount of
data, to achieve a high performance. For many real-
world applications it can be both time-consuming and
expensive to collect the required amount of data, as is
also the case for this research.
The ResNet-34 model is extended with two fully
connected layers of size 512 and 256 and finally with
a softmax layer of size 2, to get an output for each
class, normal and abnormal. The model architecture
is illustrated in figure 3.
Deep Learning-based Anomaly Detection on X-Ray Images of Fuel Cell Electrodes
327
2.4.1 Training
The anomaly detector is trained using a single Nvidia
GeForce GTX 1080 GPU. We train an instance of the
anomaly detector for each of the 4 conversion meth-
ods, which are each trained for 50 epochs. We choose
50 epochs to avoid overfitting the anomaly detector to
the small training set. All bundles of fuel cell elec-
trodes are used as training set except one, which is
used for evaluation, as described in section 3. All
layers in the ResNet-34 CNN are frozen, except for
the last fully connected layer of size 1000 (as seen in
figure 3), which is fine-tuned together with the fully
connected layers of the anomaly classifier. We use the
cross-entropy loss and an initial learning rate of 0.001
which decays every 10 epochs.
We augment the dataset with random horizontal
and vertical flips (set to 50% probability) and resize
the electrode images to height of 2000 pixels and
width of 1000 pixels.
3 RESULTS
In this section we present the results achieved by
the anomaly detector when applying the 4 conversion
methods described in section 2.3 to the fuel cell elec-
trode X-ray dataset. The anomaly detectors are evalu-
ated using the balanced accuracy metric described in
section 3.1.
3.1 Balanced Accuracy
We use the balanced accuracy metric to evaluate the
anomaly detector, as it proves useful for binary clas-
sification problems on datasets with class imbalance,
as is the case for this project. Whereas, accuracy
can be misleading if the class imbalance is great.
The balanced accuracy metric overcomes this issue by
weighting the positive and negative samples equally
significant despite one class being more numerous
than the other. This is done by adding the true pos-
itive rate (TPR) with the true negative rate (TNR) and
dividing them by 2, as can be seen in equation 3.
BALANCED ACC =
T PR + T NR
2
(3)
For a dataset with 98 positive samples and 2 neg-
ative samples, a classifier will achieve an accuracy
score of 98% by simply classifying every sample as
positive. The balanced accuracy score will only be
50% in this case.
3.2 Cross-validation Evaluation
The dataset is evaluated through cross-validation, due
to the limited size of the dataset and the nature of the
dataset where each bundle of images is coated with
a platinum catalyst solution using the same coating
method and solution mixture. This means images
from the same bundle will have a high similarity to
one another. Utilizing samples from the same bundle
as both training and testing samples will therefore in-
evitable occlude the performance of the anomaly de-
tectors.
We train the anomaly detector as described in sec-
tion 2.4.1 using all but one bundle, which is used as
test set. The same procedure is replicated until each
bundle has been evaluated individually for each of
the 4 conversion methods. Meaning a total of 4 × 12
training/evaluations are performed. For each train-
ing/evaluation run, the balanced accuracy score is cal-
culated. A combined balanced accuracy score is then
calculated for each conversion method by adding the
true positives (TP), false positives (FP), true negatives
(TN) and false negatives (FN) obtained when evaluat-
ing each of the 12 bundles, for the given conversion
method. The results can be seen in table 1.
We find that the best anomaly detection perfor-
mance is achieved by conversion method 2, with a
balanced accuracy of 85.18%. Surprisingly, we find
that method 4, which combines conversion method
1, 2 and 3 achieves the worst overall anomaly de-
tection performance. A possible explanation for this
might be that the dissimilarity between the combined
3-channel image created by method 4 and the features
of RGB images in ImageNet, which our feature ex-
tractor CNN is pre-trained on, is too great.
4 DISCUSSION
Labeling anomalies in X-ray images of fuel cell elec-
trodes is a difficult and time-consuming task, which
requires expertise in the specific domain and knowl-
edge about the severity and consequences different
anomaly types impose to the conductivity of the fuel
cell systems in which the electrodes will be used.
When labeling samples for a binary classification
problem, the person labeling must decide on which
class the sample belong to, for this work, whether an
electrode is normal or abnormal.
We find that making this decision, for some sam-
ples, is a non-trivial task prone to subjectivity. While
one expert might label the sample as normal another
expert might label the same sample as abnormal. One
solution, which was chosen for this work, is to let the
VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications
328
Table 1: The balanced accuracy (%) achieved by the anomaly detector when applying conversion method 1, 2, 3 and 4 to the
fuel cell electrode X-ray images. The performance achieved by each conversion method is measured through cross-validation,
where each bundle is used as test set individually while the remaining bundles are used as training set. The green colors
highlight the method(s) which achieved the best balanced accuracy score for each bundle. The olive-green color highlights
the method which achieved the best overall balanced accuracy score when combining the evaluations of each bundle, which
was conversion method 2.
Test Bundle Method 1 Method 2 Method 3 Method 4
Bundle 1 50.00 96.88 50.00 40.63
Bundle 2 75.00 50.00 75.00 75.00
Bundle 3 50.00 50.00 50.00 50.00
Bundle 4 90.54 58.65 67.30 67.30
Bundle 5 75.00 62.50 75.00 62.50
Bundle 6 66.15 87.05 77.56 85.13
Bundle 7 82.39 89.53 73.89 72.17
Bundle 8 50.00 50.00 100.00 50.00
Bundle 9 68.53 50.00 90.00 70.00
Bundle 10 50.00 50.00 54.17 54.17
Bundle 11 75.00 99.40 75.00 75.00
Bundle 12 53.33 80.70 75.52 67.76
Overall 82.31 85.18 85.00 81.02
more experienced expert make the final decision.
A second solution, could be to distribute the label-
ing task across many experts or non-experts and let
the label with most votes represent the sample. The
method has been described and evaluated by (Youne-
sian et al., 2020) and it has the potential to remove
subjectivity from the labels. The drawback to this so-
lution is that it can be expensive and time-consuming
and in some cases a sample might end up having
equally many votes for each class in which case an
additional solution for these cases need to be found.
Other solutions could be to simply exclude such
samples from the dataset or to introduce a third class
which represents samples which are undecidable, if
one or more experts disagree. Such a class would
in our case be small and cause a highly imbalanced
dataset.
5 CONCLUSIONS
This paper proposed an anomaly detector using a
Deep Convolutional Neural Network, an extended
ResNet-34 model, for detecting anomalies in X-ray
images of fuel cell electrodes. For this purpose, a
dataset with normal and abnormal fuel cell electrodes
X-ray images was created. The dataset consists of
12 bundles of images with a total of 714 X-ray im-
ages. The anomaly detector is used by the company
Serenergy for automatizing a time-consuming man-
ual quality control of the fuel cell electrode X-ray im-
ages. The anomaly detector was trained and evaluated
through cross-validation where a single bundle of im-
ages is used as test set and the remaining bundles are
used as training set. The proposed anomaly detector
was trained and evaluated using 12 × 50 epochs with
a Nvidia GeForce 1080 GTX GPU and the PyTorch
deep learning framework. We compared 16-bit to 8-
bit conversion methods for pre-processing the X-ray
images. We find that performing histogram equaliza-
tion with upper- and lower bounds set by the max-
imum and minimum pixel values calculated across
the entire dataset achieves a better performance than
when using local maximum and minimum as upper-
and lower bounds calculated for each individual im-
age. We achieve a balanced accuracy of 85.18%.
In the future, we will continue to explorer ap-
proaches for performing more accurate anomaly de-
tection in X-ray images. Potential improvements
could be achieved by using variations of weighted
cross-entropy loss and data augmentation to cope
with the imbalanced dataset and by utilizing differ-
ent histogram equalization methods e.g., the DHE al-
gorithm. Further, we see a potential in using CNNs
pre-trained on large-scale gray-scale image datasets
for classifying X-ray images. Whereas most CNNs
today are pre-trained on RGB image datasets e.g., Im-
ageNet.
ACKNOWLEDGEMENTS
We would like to thank Serenergy for their contribu-
tions, collaboration and dataset for this paper. We
would also like to thank Ambolt Aps for initiating and
facilitating the collaboration with Serenergy.
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