An Investigation of Deep-Learned Features for Classifying Radiographic

Images of COVID-19

Pedro Lucas Miguel

, Adriano Mauro Cansian

1 a

, Guilherme Botazzo Rozendo

Giuliano Cardozo Medalha

, Marcelo Zanchetta do Nascimento

3 b

and Leandro Alves Neves

1 c

Department of Computer Science and Statistics (DCCE), S

ao Paulo State University (UNESP), Rua Crist

ao Colombo,

2265, 15054-000, S

ao Jos

e do Rio Preto-SP, Brazil

WZTECH NETWORKS, Avenida Romeu Strazzi (room 503-B), 325, 15084-010, S

ao Jos

e do Rio Preto-SP, Brazil

Faculty of Computer Science (FACOM), Federal University of Uberl

andia (UFU), Avenida Jo

ao Naves de

Avila 2121,

Bl.B, 38400-902, Uberl

andia-MG, Brazil

Keywords:

Radiographic Images, COVID-19, Convolutional Neural Networks, Deep-Learned Features, RelieF.

Abstract:

In this proposal, a study based on deep-learned features via transfer learning was developed to obtain a set

of features and techniques for pattern recognition in the context of COVID-19 images. The proposal was

based on the ResNet-50, DenseNet-201 and EfﬁcientNet-b0 deep-learning models. In this work, the chosen

layer for analysis was the avg pool layer from each model, with 2048 features from the ResNet-50, 1920

features from the DenseNet0201 and 1280 obtained features from the EfﬁcientNet-b0. The most relevant

descriptors were deﬁned for the classiﬁcation process, applying the ReliefF algorithm and two classiﬁcation

strategies: individually applied classiﬁers and employed an ensemble of classiﬁers using the score-level fusion

approach. Thus, the two best combinations were identiﬁed, both using the DenseNet-201 model with the

same subset of features. The ﬁrst combination was deﬁned via the SMO classiﬁer (accuracy of 98.38%)

and the second via the ensemble strategy (accuracy of 97.89%). The feature subset was composed of only

210 descriptors, representing only 10% of the original set. The strategies and information presented here are

relevant contributions for the specialists interested in the study and development of computer-aided diagnosis

in COVID-19 images.

1 INTRODUCTION

The analysis of radiographic images is one of the

stages widely used in medicine to deﬁne diagnostics

and prognostics for different diseases. For instance,

when the investigation of COVID-19 is considered,

radiographic images were commonly used to identify

the possible patterns of this disease. Thus, computa-

tional systems can be developed and applied to sup-

port specialists in this process (Organization, 2023),

with the deﬁnition and classiﬁcation of the main de-

scriptors. This type of application has been widely in-

vestigated to deﬁne a computer-aided diagnosis, with

multiple methodologies (Su et al., 2022; Song et al.,

2022; Deb et al., 2022; Tuncer et al., 2020).

The process of analyzing radiographic images us-

https://orcid.org/0000-0003-4494-1454

https://orcid.org/0000-0003-3537-0178

https://orcid.org/0000-0001-8580-7054

ing convolutional neural networks (CNN) is a widely

explored issue in the ﬁeld of image processing, with

important applications in the study of COVID-19

images. In this context, (Kedia et al., 2021) pre-

sented a CNN named CoVNet-19. This model was

obtained using various techniques, such as transfer

learning and an ensemble strategy, providing an accu-

racy of 99.71% in the context of radiographic images.

Furthermore, another strategy presented by (Ashour

et al., 2021) involved an ensemble method, with mod-

els based on bag-of-features. The choice of this type

of model considered the variations and spatial orienta-

tions of the images. This proposal was able to provide

an accuracy of 98.60% in the classiﬁcation of radio-

graphic images of lung regions. Finally, the authors

in (Deb et al., 2022) used an ensemble with multi-

ple pre-trained models, such as VGGNet, GoogleNet,

DenseNet and NASNet and provided an accuracy of

98.58%.

Miguel, P., Cansian, A., Rozendo, G., Medalha, G., Zanchetta do Nascimento, M. and Neves, L.

An Investigation of Deep-Learned Features for Classifying Radiographic Images of COVID-19.

DOI: 10.5220/0012038500003467

In Proceedings of the 25th International Conference on Enterprise Information Systems (ICEIS 2023) - Volume 1, pages 675-682

ISBN: 978-989-758-648-4; ISSN: 2184-4992

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

675

Despite the existence of several studies that pro-

posed speciﬁc networks to solve the problem of clas-

siﬁcation in focus, other studies also address the use

of already consolidated networks. In (Walvekar et al.,

2020), the authors proposed an approach that em-

ployed a ResNet-50 model which was pre-trained on

the ImageNet dataset with transfer learning. The ex-

periments were deﬁned via a public dataset (Cohen

et al., 2020), with 359 radiographic images from the

lung region that indicated the presence of pneumonia

caused or not by COVID-19. This proposal was able

to indicate an accuracy of 96.23%. The study pre-

sented by (Shamila Ebenezer et al., 2022) involved

the EfﬁcientNet-b0 model with enhanced images via

Laplace algorithms and wavelet transform. The main

goal was to verify the impact of these adjustments

on the complete training of the CNN models. This

approach provided an accuracy of 94.56%, consid-

ering radiographic images of two classes infected by

COVID-19 and not infected with this disease.

In this context, there were few studies based on

a detailed analysis of deep features, a fact that moti-

vated the development of this proposal. For instance,

the model presented by (Tuncer et al., 2020) consid-

ered a strategy responsible for deﬁning features called

residual exemplar local binary pattern. The features

were ranked via the RelieF algorithm and classiﬁed

with multiple methods. This proposal achieved an ac-

curacy of 100% through the SVM classiﬁer, exploring

representative radiographic images of Covid-19 and

healthy. The study proposed by (Rajpal et al., 2021)

used the ResNet-50 model to extract a subset of the

most relevant features, considering the 2048 attributes

present in the avg pool layer. The authors manu-

ally evaluated a group of 252 features. This subset

was studied via principal component analysis (PCA).

A new subset was deﬁned with the 64 most impor-

tant attributes. Finally, these features were reduced

again after being used as input to a feed-forward net-

work, which selected only 16 features. The model

provided an accuracy of 94.40%, considering a total

of four classes: viral pneumonia; bacterial pneumo-

nia; COVID-19 and not infected.

In this study, a proposal based on deep-learned

features via transfer learning is deﬁned to indicate

the main combinations of attributes and techniques

for the classiﬁcation and pattern recognition in ra-

diographic images of COVID-19. The presented

approach explores three different CNN models for

COVID-19 images. The corresponding deep-learned

features were obtained, and a ranking method was

applied to maximize the classiﬁcation performance.

The analysis was deﬁned via an ensemble classiﬁ-

cation, considering the score-level fusion approach.

This proposal allows for identifying the main asso-

ciations with competitive results concerning the spe-

cialized literature. The main contributions presented

here are:

• A strategy capable of identifying the main deep-

learned features, with competitive results con-

cerning consolidated and widely explored meth-

ods in the context of COVID-19;

• A model capable of identifying the main combi-

nations via a reduced number of features;

• Indications of associations and conditions for the

improvement of computer-aided diagnosis with a

focus on the analysis of radiographic images of

COVID-19.

2 METHODOLOGY

The proposed method was divided into three steps.

The ﬁrst step was deﬁned to extract the deep-learned

features, exploring speciﬁc layers of relevant CNN

models. The second stage was proposed to compose

and select the most relevant features. Finally, the third

step (analysis and knowledge extraction) was indi-

cated to guarantee the analysis of the selected fea-

tures through two experiments: predictions through

classiﬁers applied individually to each set of features;

results via an ensemble classiﬁcation process. An

overview of the proposal is shown in Figure 1.

2.1 Dataset

This investigation explored representative radio-

graphic images of COVID-19 that were obtained

through a public dataset (Cohen et al., 2020). This

dataset consists of images from different public

sources. It is important to highlight that this dataset

is considered dynamic, therefore, the number of sam-

ples that represent each class, as well as the type

of those, are updated frequently. The used version

for this study explored a total of 2.040 images di-

vided into two classes: Healthy with 1602 samples

and COVID-19 with 438 samples. It is important to

note that the samples belonging to the healthy class

are people who have been properly tested as non-

carriers of COVID-19. Also, the images are repre-

sentative of segmented regions, where it is possible

to observe only the patient’s lungs. In addition, the

images have multiple sizes, therefore, in the training

phase of the fully connected layers of each model, the

images were resized to 224 pixels of height and 224

pixels of width. Figure 2 illustrates available images

in the dataset explored here.

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Figure 1: An overview of the proposed method for analyz-

ing COVID-19 images.

Figure 2: Illustration of images explored in our study from

the dataset presented by (Cohen et al., 2020).

2.2 Step 1: Feature Extraction

The COVID-19 images were analyzed through three

deep-learning models. The chosen architectures were

a residual neural network with 50 layers (ResNet-

50), dense convolutional neural network with 201

layers (DenseNet-201) and the EfﬁcientNet baseline

(EfﬁcientNet-b0) (He et al., 2016; Huang et al., 2016;

Tan and Le, 2020).

2.2.1 Selected Layers

In this investigation, the features were obtained from

the ResNet-50, DenseNet-201, and EfﬁcientNet-b0

neural networks pre-trained on the ImageNet dataset

(Deng et al., 2009). The adopted strategy in this

proposal considered the indications presented by

(Pereira dos Santos and Antonelli Ponti, 2019). In

this case, the convolutional features from the initial

layers were capable to bring local information about

the analyzed images, like low levels of forms, bor-

ders, and colors. While features from the last layers

tend to provide global descriptors. Thus, the convolu-

tional attributes obtained from the networks were iso-

lated through a strategy that allows the storing of the

output from the layers into auxiliary structures. These

structures were used as input to the feature selection

method, capable of identifying the most relevant and

generalizable feature sets. In this study, the avg pool

layer of each network was selected for the analyses,

considering that this layer can provide contributions

related to the local and global information of the im-

ages. A summary of the number of features used in

our proposal is illustrated in Table 1, considering each

layer here explored.

Table 1: Number of features of each avg pool layer for each

network model.

Network Number of features

ResNet-50 2048

DenseNet-201 1920

EfﬁcientNet-b0 1280

2.3 Step 2: Deﬁnition of the Most

Relevant Features

The convolutional attributes were deﬁned through n-

dimensional matrices called N

[...], where i represents

one of the layers under investigation. Each column

in the N

matrix was sequentially sorted into feature

vectors K

[...], where the number of elements in N

equal to the number of elements in K

. From K

, the

RelieF algorithm was applied to rank the most rele-

vant deep features. Thus, each set K

was distributed

into subsets F in relation to j best-ranked attributes.

It is important to highlight that each subset was de-

ﬁned through different cutoff points, considering an

approach successfully applied to classify colorectal

histological images (Ribeiro et al., 2019). However,

our proposal identiﬁed the most relevant subset in a

wider attribute space (with the ﬁrst 250 best-ranked

attributes), mainly due to the differences present in

the context investigated here, for instance, with more

attributes and samples. Thus, the initial subset was

deﬁned with j = 10, being incremented in steps of

10 attributes. Moreover, this limited number of deep

features was established to obtain optimized solutions

(with a reduced number of features) in order to mini-

mize the presence of overﬁtting.

An Investigation of Deep-Learned Features for Classifying Radiographic Images of COVID-19

677

2.4 Step 3: Analysis and Knowledge

Extraction

This step considered two experiments: The ﬁrst ex-

periment was deﬁned to investigate the discrimina-

tive capabilities of the deep-learned features through

multiple classiﬁers (individually applied). The used

classiﬁers were: function based, such as support vec-

tor machine (SVM) and sequential minimal optimiza-

tion (SMO) (Vapnik, 1963; Platt, 1998); lazy learning

based, such as locally weighted learning (LWL) and

instance based k (IBk) (Frank et al., 2003; Atkeson

et al., 1996); and decision tree based, such as ran-

dom forest (RaF) and random tree (RaT) (Breiman,

2001; Frank and Kirkby, 2023). The goal of this anal-

ysis was to obtain capable models in order to obtain

the best performances, with the lowest number of fea-

tures, that could contribute to the comprehension of

the explored context.

The second experiment was addressed to complete

the analyses previously indicated, exploring an en-

semble strategy with the classiﬁers used in the ﬁrst

step. The adopted approach was a score-level fusion

in order to explore the strongest points of each clas-

siﬁer (Ross and Nandakumar, 2009). As in the ﬁrst

experiment, the 250 deep features were analyzed with

sets arranged for every ten features.

In addition, a 10-fold cross-validation method was

applied to validate our experiments. Finally, the re-

sults were veriﬁed through different metrics, such as

accuracy (ACC), F-measure and the area under the re-

ceiver operating characteristic curve (AUC).

2.4.1 Development Environment

Both the algorithm responsible to obtain the deep-

learned features, and the algorithm to explore the

ResNet, DenseNet, and EfﬁcientNet models were de-

veloped in the Python language. Especially, to ex-

plore the models, the Pytorch framework was used

(Paszke et al., 2019). This framework provides the

ResNet-50, DenseNet-201, and EfﬁcientNet-b0 previ-

ously coded and trained on the ImageNet dataset, con-

sidering the speciﬁcations presented by the authors of

each model (He et al., 2016; Huang et al., 2016; Tan

and Le, 2020).

To test the performance of each network model,

the framework Pytorch Ignite was also used (Fomin

et al., 2020), being so responsible for the training of

the last fully connected layer of each model. The pro-

posal was realized in a notebook with an Intel Core i5-

10210U, 16 GB of RAM, NVIDIA MX 110 graphics

card, and the Windows 11 operating system. Finally,

the Weka 3 package was used to classify the deep-

learned features, as well as to obtain all the metrics

used by this project (Witten et al., 2011; Hall et al.,

2009).

3 RESULTS

The approach was applied to analyze radiographic

images of COVID-19 with two experiments via the

deep-learned features of avg pool layers, ResNet-50,

DenseNet-201 and EfﬁcientNet-b0 models. Thus,

taking into account the accuracy metric as an initial

reference, Figure 3 shows the obtained results with

the ensemble of classiﬁers.

Figure 3: ACC values (%) for each subset of features,

exploring the ensemble of classiﬁers and avg pool layers

(ReseNet-50, DenseNet-201, and EfﬁcientNet-b0).

From the obtained results in the different com-

positions of deep features, it was noticed that the

DenseNet-201 model indicated the best performance,

with a subset of 210 attributes and an ACC value of

97.89%. In this case, the F-measure value was 0.979

and the AUC rate was 0.9941 (a performance close

to that of an ideal system). These results are interest-

ing and indicate an important contribution of the pro-

posed methodology, since it was possible to identify

a combination of techniques capable of providing an

acceptable solution with only 10.94% of the original

features, avoiding overﬁtting in this model. Also, this

combination indicated a class distinction capability.

The second experiment was deﬁned with a combi-

nation of the deep-learned features via SVM, SMO,

LWL, IBK, RaF and RaT classiﬁers, with perfor-

mances obtained individually. The results are pre-

sented in Figures 4, 5, and 6, considering the ResNet-

50, DenseNet-201 and EfﬁcientNet-b0 models, re-

spectively.

Taking into account the results in Figure 4

(ResNet-50 model), the SMO classiﬁer in combina-

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Figure 4: Accuracy values (%) obtained through the differ-

ent subsets of features with each classiﬁer: ResNet-50 and

the corresponding avg pool layer.

Figure 5: Results obtained of ACC values (%) provided by

different subsets of features with each classiﬁer: DenseNet-

201 and the corresponding avg pool layer.

tion with 200 deep features indicated the main strat-

egy, with an ACC of 94.17%, F-measure of 0.9410

and an AUC of 0.9056. In relation to the DenseNet-

201 model (Figure 5), the SMO classiﬁer with 210

features indicated the best combination in this study,

with an ACC of 98.38%, F-measure of 0.984 and

an AUC value of 0.9814. When the EfﬁcientNet-b0

model is considered (Figure 6), the best combina-

tion was also obtained through the SMO classiﬁer, but

with a fewer number of features (150 features). The

ACC value was 97.16%, an F-measure of 0.972 and

an AUC rate of 0.9603. It is important to emphasize

that the SMO classiﬁer presented the best results in

each test, indicating a relevant pattern in the context

investigated here.

In order to complement this analysis, Table 2

summarizes the best ﬁve results, considering all tests

Figure 6: Accuracy values (%) achieved via different sub-

sets of features with each classiﬁer: EfﬁcientNet-b0 and the

corresponding avg pool layer.

deﬁned here.

From Table 2, it is observed that the ﬁrst position

is a solution involving a single classiﬁer. However,

the ensemble strategy appears in three of the ﬁve best

combinations observed in this study. Moreover, in the

ﬁrst two positions, the solutions were obtained with

the same subset: 210 deep-learned features. Also,

considering the ACC metric, the difference is less

than 1% among these two solutions. The second-best

combination can be highlighted for its AUC (0.9941),

surpassing the result obtained via the best individual

combination, with the advantage of using an ensem-

ble of classiﬁers.

In order to contextualize other contributions of

this study, the best result according to the ACC met-

ric (Table 2) was observed in relation to the best

results obtained via ResNet-50, DenseNet-201 and

EfﬁcientNet-b0 networks (all applied directly to clas-

sify the same set of images). It is important to note

that the CNN models were pre-trained into the Im-

ageNet dataset. Thus, it was necessary to train the

last fully connected layer of each network to adjust

the number of classes in this analysis (Healthy and

COVID-19). The training of the last fully connected

layer was deﬁned with a total of 50 epochs, consider-

ing the details presented by (He et al., 2016). Also, in

order to validate the results of each epoch, the orig-

inal dataset was divided into training and test sets,

with an 80-20 split. The ACC, F-measure and loss

metrics were considered in these experiments. Figure

7 illustrates the obtained results through the accuracy

values for each epoch. The DenseNet-201 provided

the best result with 33 epochs, an accuracy value of

96.57%, a loss of 0.12 and an F-measure of 0.94. On

the other hand, this performance is lower in compar-

An Investigation of Deep-Learned Features for Classifying Radiographic Images of COVID-19

679

Table 2: Deﬁnitions of the ﬁve best associations to classify the COVID-19 images, with the corresponding performances

(ACC, F-measure and the AUC).

Ranking Network Combination ACC (%) F-measure AUC

◦

DenseNet-201 210 Features, SMO classiﬁer 98.38% 0.984 0.9814

◦

DenseNet-201 210 Features, Ensemble 97.89% 0.979 0.9941

◦

ResNet-50 200 Features, Ensemble 97.70% 0.977 0.9932

◦

ResNet-50 200 Features, SMO classiﬁer 97.65% 0.977 0.9684

◦

EfﬁcientNet-b0 250 Features, Ensemble 97.59% 0.970 0.9882

ison to those via proposed associations, as shown in

Table 2. This information indicates another important

contribution of this study.

Figure 7: Accuracy values (%) obtained after classifying the

images with the ResNet-50, DenseNet-201 and EfﬁentNet-

b0 models, exploring results with up to 50 epochs.

Finally, the relevance of the best result in relation

to the specialized Literature was veriﬁed. It is noted

that the explored techniques consider different strate-

gies and datasets from those explored here. Thus,

these facts do not allow a direct comparison between

the approaches but provide an illustrative overview of

the model through some important works in this area,

Table 3.

From Table 3, it is veriﬁed that the best combi-

nation from our proposal was capable of obtaining

results between the main works available in the spe-

cialized Literature. Even with the less expressive re-

sult compared to those indicated by some methods

(Tuncer et al., 2020; Kedia et al., 2021; Ashour et al.,

2021; Deb et al., 2022), the advantage of the proposed

methodology was a solution with competitive results

via a reduced number of features, making the knowl-

edge more comprehensive for specialists interested in

computer-aided diagnosis.

4 CONCLUSIONS

This work presented a detailed study of deep-

learned features via transfer learning, considering

avg pool layer from the ResNet-50, DenseNet-201,

and EfﬁcientNet-b0 networks. The best combinations

were identiﬁed with a full understanding of the subset

of deep-learned features for the classiﬁcation and pat-

tern recognition in COVID-19 images. The reduced

set of features was obtained via a strategy based on the

RelieF algorithm, with the use of multiple classiﬁers

and a robust ensemble strategy. This type of associa-

tion and the obtained information through our exper-

iments are relevant contributions presented here. For

instance, the best two combinations were obtained

from the DenseNet-201 model, using the same sub-

set: 210 deep-learned features. This total of features

represented only 10.94% of the original set. In the

test applying individual classiﬁers, the SMO algo-

rithm was capable of indicating the best results, with

an ACC of 98.38%. In the test with the ensemble

strategy, the result was an ACC of 97.89%, a value

subtly lower than that indicated via the SMO associa-

tion.

When the best results were compared to those ob-

tained with the use of complete neural networks, it

was veriﬁed that the solutions with our proposal sur-

passed the performances provided by the CNN mod-

els. Finally, when the results were observed in rela-

tion to some related works, our study was able to de-

ﬁne models with performances among the available

specialized literature, providing relevant information

regarding the main associations via a reduced number

of deep features. Therefore, we believe that this con-

tribution is useful for specialists interested in inves-

tigating computational systems for radiographic im-

ages of COVID-19.

In future works, it is intended to explore different

architectures of CNNs and perform the combined use

with descriptors based on fractal techniques, in ad-

dition to the indication of techniques to highlight the

best-ranked attributes in the activation maps (class ac-

tivation mapping).

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Table 3: Illustrative overview of the different methods available in the specialized literature to investigate the context of

radiographic images of COVID-19.

Reference Approach ACC (%) AUC

(Tuncer et al., 2020)

ResExLBP, RelieF,

SVM

100% -

(Kedia et al., 2021)

CoVNet-19, Ensemble Learning,

Transfer learning

99.71% 0.99

(Ashour et al., 2021)

Bag of features, Ensemble

98.60% 0.98

(Deb et al., 2022) Multi model ensemble architecture 98.58% 0.95

Proposed

Transfer learning, DenseNet-201,

RelieF, SMO

98.38% 0.98

(Walvekar et al., 2020)

ResNet-50

96.23% 0.96

(Shamila Ebenezer et al., 2022)

EfﬁcientNet-b0, Image Enchancement

94.56% 0.93

(Rajpal et al., 2021)

Handpicked Features, ResNet-50

94.40% 0.97

(Hemdan et al., 2020)

DenseNet-121, VGG19

90.00% 0.90

ACKNOWLEDGEMENTS

This study was ﬁnanced in part by the: National

Council for Scientiﬁc and Technological Develop-

ment CNPq (Grants #313643/2021-0, #311404/2021-

9 and #120993/2020-1); State of Minas Gerais Re-

search Foundation - FAPEMIG (Grant #APQ-00578-

18); WZTECH NETWORKS, S

ao Jos

e do Rio Preto,

ao Paulo.

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