Novel Pre-processing Stage for Classiﬁcation of CT Scan Covid-19

Images

D. Vijayalakshmi

1 a

, Malaya Kumar Nath

1 b

and Madhusudhan Mishra

2 c

Department of ECE, National Institute of Technology Puducherry, Karaikal, India

Department of ECE, North Eastern Regional Institute of Science and Technology, Nirjuli, Arunachal Pradesh, India

Keywords:

CT Image Enhancement, Gradient based Edge Information, Pre-processing for Medical Images, Contrast

Improvement Index.

Abstract:

An accurate evaluation of computed tomography (CT) chest images is crucial in the early-stage detection of

Covid-19. The accuracy of a diagnosis is determined by the imaging modality used and the images’ consis-

tency. This paper describes a gradient-based enhancement algorithm (GCE) for CT images that can increase

the visibility of the infected region. Using a multi-scale dependent dark pass ﬁlter aims to increase contrast

while preserving information and edge details of the infected area. Joint occurrence between the edge details

and pixel intensities of the input image is calculated to construct a cumulative distribution function (CDF). To

obtain the contrast improved image, the CDF is mapped to the uniform distribution. The GCE approach is

tested on the CT Covid database, and performance metrics like the contrast improvement index (CII), discrete

entropy (DE), and Kullback-Leibler distance (KL-Distance) are used to evaluate the results. Compared to

other techniques available in the literature, the GCE approach produces the highest CII and DE values and

has more uniformity. To check the suitability of the enhancement algorithm in terms of pre-processing, a pre-

trained AlexNet is employed for the classiﬁcation of Covid-19 images. The ﬁnding shows an improvement of

7% in classiﬁcation accuracy after enhancing the Covid-19 images using the GCE technique.

1 INTRODUCTION

Image acquisition, image processing, and image dis-

play play a role in medical image diagnosis. Various

types of noise may be introduced into medical images

during the acquisition process. The diagnostic pro-

cess will not be possible with these images. Image

enhancement methods can be used to efﬁciently elim-

inate noise and improve the quality of input images

to be used for disease detection. A pre-processing

phase in medical image processing is removing inher-

ent noise from the image or enhancing the picture’s

contrast.Low contrast images are also insufﬁcient for

disease diagnosis (Malik et al., 2015).

Covid-19 is caused by SARS-CoV-2 and declared

a pandemic by the World Health Organization (WHO)

in March 2020. Covid-19 is a highly contagious

virus that can lead to fatal acute respiratory distress

syndrome (ARDS). Controlling the spread of Covid-

19 needs early identiﬁcation and diagnosis. The

https://orcid.org/0000-0001-5567-4019

https://orcid.org/0000-0002-1959-6452

https://orcid.org/0000-0002-5891-7984

reverse-transcription polymerase chain reaction (RT-

PCR) test is the most popular screening tool for de-

tection. However, it is a time-consuming procedure,

and several studies have shown that it has poor sensi-

tivity in the early stages. Computer tomography (CT)

and chest X-ray imaging can be used as an alternative

to the RT-PCR test for precise diagnostic and various

stages of disease evolution. The use of readily avail-

able imaging techniques in all Indian hospitals can be

a faster and less expensive way of diagnosing Covid-

19 (Nath et al., 2020).

Computed tomography (CT) imaging technology

is becoming increasingly relevant in the computerized

diagnostics system for medical assessment and early

diagnosis. However, noise, storage, and transmission

loss often disturb the digital image quality produced

by recent imaging devices, which results in noisy low-

contrast images that can degrade the effects of sub-

sequent measures such as segmentation, feature ex-

traction, and diagnosis. As a result, image quality

enhancement, especially contrast enhancement, has

piqued the interest of researchers over the last two

decades. There are a variety of contrast enhance-

Vijayalakshmi, D., Nath, M. and Mishra, M.

Novel Pre-processing Stage for Classiﬁcation of CT Scan Covid-19 Images.

DOI: 10.5220/0010625200870094

In Proceedings of the 18th International Conference on Signal Processing and Multimedia Applications (SIGMAP 2021), pages 87-94

ISBN: 978-989-758-525-8

ment techniques available, such as classical histogram

equalization (HE) and others (Chi et al., 2019).

For image enhancement, Local Histogram Equal-

ization (LHE)(Celik, 2012) is one of the most widely

used techniques. The entire image is encompassed in

a window in LHE, with the histogram locally equal-

izing the actual pixel inside the given window. Be-

cause of the complexity and variety of window sizes,

several algorithms have been created to improve the

efﬁciency of HE. In 1997, Kim developed intensity

preserving bi-histogram equalization (BBHE) to ad-

dress HE’s mean brightness shifting issues. The in-

put low contrast image’s histogram is divided in half

by the average pixel intensity, and the sub-histograms

are equalized separately by BBHE (Kim, 1997).

Following Brightness preserving BHE, Dualistic

Sub-Image HE (DSIHE) (Wang et al., 1999) was de-

veloped, which distinguishes the histogram of the

input image by using the median value rather than

the mean value. Recursive Mean-Separate Histogram

Equalization (RMSHE) and Recursive Sub-Image

Equalization (RSIHE) have been developed as gener-

alization schemes for BBHE and DSIHE. RSIHE and

RMSHE produce 2

sub-histograms by recursively di-

viding the input histogram using the mean and me-

dian values (Sim et al., 2007). The optimum value

for ’r’ is the most difﬁcult to describe. When ’r’ is

high, the resultant image will be nearly identical to

the original image, with no enhancement (Vijayalak-

shmi et al., 2020).

The algorithms listed above are primarily con-

cerned with preserving mean brightness. By incorpo-

rating clipping limits into their transformation feature,

later BHE algorithms were designed to minimize over

enhancement. These cutting limits are the quantitative

parameters derived from the input image. (Tang and

Isa, 2014). The feature-preservation BHE (CEF) pro-

cess was maintained the image features through a con-

trast improvement. It employs gamma transformation

to reduce the effect of over enhancement. It removes

histogram pits using histogram addition (Wang and

Chen, 2018).

Adaptive cutting limit and detail improving modi-

ﬁcations are employed in edge enhancing BHE (Tang

and Isa, 2014). Cutting limits are determined from

the entropy values of the segmented histogram, and

detail improvement is achieved by measuring the di-

rected ﬁlter’s linear coefﬁcients for each pixel in the

input image. Finally, the ﬁlter coefﬁcients are used to

create the enhanced image (Mun et al., 2019).

Due to the inability to use dynamic grayscale

in the above-mentioned bi-histogram methods, two

dimesional histogram-based methods generate im-

ages with high contrast. The intensity values

with their spatial positions are employed in two

-dimensional histogram-based techniques. Two-

dimensional HE (2DHE) utilizes the correctly cho-

sen spatial neighbourhood’s contextual information to

produce an adequately enhanced image (Celik, 2012).

However, a large number of trials are required to

achieve the proper size of the neighbourhood. The

transformation function of spatial entropy-based con-

trast enhancement (SECE) utilizes the spatial loca-

tion along with the number of occurrences that helps

the pixels to occupy the entire dynamic range (Ce-

lik, 2014). However, it has no power over the rate of

enhancement, which may result in over-enhancement

(Chen et al., 2019)(Cai et al., 2018). Residual spa-

tial entropy-based enhancement (RESE) method uses

non-linear mapping based on residual entropy for

contrast enhancement, which may result in a minor

improvement in contrast (Celik and Li, 2016).

Joint histogram equalization (JHE) has solved the

challenges of the RESE. The joint histogram (JH)

measures the gray values and information in the spa-

tial neighborhood that occur together (Agrawal et al.,

2019).

As shown in the above discussion, bi-histogram

approaches do not use the entire complex grayscale,

resulting in minor contrast change. On the other

hand, the two-dimensional histogram-based methods

use the whole of dynamic grayscale, but the inten-

sity distribution after enhancement is not standard-

ized. In the processed picture, this results in noisy

appearances.

Most of the authors have used unprocessed images

for Covid-19 classiﬁcation by utilizing various pre-

trained networks such as AlexNet, Googlenet, VGG-

16, and VGG-19, etc.,(Nath et al., 2020). However,

the uneven distribution of intensities and fewer inten-

sity values lead to poor discrimination of infected and

uninfected regions in the CT scan images. Therefore,

it may result in decreasing the classiﬁcation accuracy

of the Covid-19 diagnosis. Nevertheless, images can

be pre-processed for differentiating the infected re-

gions from uninfected regions to overcome the prob-

lem,(Jeevakala and Therese, 2018). So in this paper, a

gradient-based contrast enhancement is suggested for

pre-processing the CT scan Covid images.

The main goal of the GCE technique is to improve

contrast while reducing artifacts, maintaining edges,

and avoiding over-enhancement. The following are

the critical contributions made in this paper:

1. The innovative gradient-based contrast enhance-

ment technique approaches multiscale analysis by

extracting image information at multiple levels of

CT scan images.

SIGMAP 2021 - 18th International Conference on Signal Processing and Multimedia Applications

2. A ﬁlter is used to detect essential image informa-

tion and to prioritize pixel differences with their

neighbours.

3. Reference and non-reference quantitative metrics

verify subjective analysis of GCE technique’s

supremacy over traditional state-of-the-art algo-

rithms.

4. To analyse the performance of Gradient based en-

hancement algorithm in the ﬁeld of machine in-

telligence, pre-trained AlexNet is used for clas-

siﬁcation of enhanced Covid images against the

unprocessed Covid images.

The remainder of the paper is structured as follows:-

In Section 2, gradient based enhancement methodol-

ogy and the network used for their classiﬁcation are

discussed. Experimental analysis of GCE technique

in comparison with some of the existing contrast en-

hancement algorithms and the assessment of GCE in

the ﬁeld of machine vision are summarized in Section

3. Finally, Section 4 concludes the paper.

2 METHODOLOGY

This section describes the classiﬁcation of Covid-19

from CT images of the chest by using a pre-trained

AlexNet followed by a pre-processing stage. The pro-

posed method is represented in Figure 1. First, the im-

ages are pre-processed by the gradient-based contrast

enhancement algorithm. Then, the enhanced images

are fed to the pre-trained network for classiﬁcation.

Figure 1: Block diagram for the assessment of contrast en-

hancement techniques.

The detail description of the blocks represented in

Figure 1 is described in the following sub-sections.

2.1 Gradient based Contrast

Enhancement

Gradient based contrast enhancement is divided into

four sections: gradient image calculation, joint his-

togram computation, discrete function enumeration,

and equalized histogram determination using a map-

ping function.

The following two measures are used to create the

gradient image:

1. To obtain edge information, a ﬁlter is employed at

multiple scales of the low contrast image.

2. The geometric mean value obtained from multi-

scale ﬁltered images results in a gradient image.

The decomposition of low contrast input image I

is obtained by employing the Gaussian pyramid. The

dimension of the input image is M×N. In each sub-

sequent image, the decimation process is utilized by

halving the sampling rate. Thus, for each decomposi-

tion, a set of pictures in multi-scale will be available,

including the original image.

Convoluting a 5×5 mask with the bottom level

image in the pyramid yields the ﬁrst level decom-

posed image(Burt and Adelson, 1983). Thus, the

mask is denoted as:

m = [0.25 − 0.5a,0.25,a,0.25,0.25 − 0.5a] (1)

m(0) = a; m(1) = m (−1) = 0.25;

m(2) = m (−2) = 0.25 − 0.5a;

(2)

where a is considered as 0.375. The 2-D coefﬁcients

are generated by

m(k, l) = m (k).m (l) (3)

To obtain the next level (l − 1) image, the 2-D

coefﬁcients are convoluted with the input image and

decimated by a factor of 2

l−1

. It is denoted by:

l−1

∑

i=−2

∑

j=−2

m(i, j).J

(x + i,y + j ) (4)

Images obtained from the pyramid are ﬁltered by

a dark pass ﬁlter (Wu et al., 2017). It is deﬁned as:

f (x,y) = −

∑

∈N(x,y)

min





(x,y) − J





L − 1





(5)

where N (x,y) denotes the 4-neighbours of the centre

pixel (x, y) and L repesents the highest pixel inten-

sity value of the input image. The gradient image is

obtained by taking the geometric mean of ﬁltered out-

puts.

G(x,y) =

∏

i=1

max(U ( f (x, y)) ,ε)

(6)

where U (.) denotes the upsampling by factor of 2

l−1

The joint occurrence of the intensities is measured

using the input image’s distinct pixel values and the

gradient image’s distinct pixel values.

Jh =

{

Jh (p,q) ; 1 ≤ p ≤ P,1 ≤ q ≤ Q

}

(7)

where P and Q denotes the number of distinct gray

values of the low contrast and the gradient image, re-

spectively.

Jh (p,q) =

{

count; f or I (x,y) = p & G (x, y) = q

}

(8)

Novel Pre-processing Stage for Classiﬁcation of CT Scan Covid-19 Images

From the joint occurrence, the CDF is calculated as

F (p, q) =

∑

i=0

∑

j=0

Jh (i, j) (9)

where F (p,q) represents the CDF. The CDF is used

to create a transformation which is given below:

(p,q) =

((L−1)×(F(p,q)−F (p,q)

))

(M×N)−1

(10)

where

rounds the values to the closest integer,

(p,q) represents the equivalent pixel value which

substitutes the given value whenever I (x,y) = p &

G(x,y) = q, F(p, q)

represents the smallest value of

the CDF.

The mapping fuction is used to result in the en-

hanced image, which is denoted as:

{

(p,q); 1 ≤ p ≤ P,1 ≤ q ≤ Q

}

(11)

Finally, an improved image is created by substitut-

ing equivalent intensities for the speciﬁed intensities

from JH

, that comprises all equivalent intensities

based on potential input and gradient image joint oc-

currences.

2.2 Image Classiﬁcation

The enhanced images are fed to the pre-trained

AlexNet for image classiﬁcation. The basic building

blocks of the network are convolutional, max pool-

ing and fully connected layers. It has eight learnable

layers. ReLU is used as an activation function in all

layers. Output layer uses softmax activation. In this

work, the tune-able parameters like mini batch size,

learning rate and the number of epochs are chosen as

32, 1e-5 and 20, respectively.

3 RESULTS AND DISCUSSION

The improved visual quality of images is required in

the medical imaging system for diagnosing abnormal-

ities in any part of the human body. It is possible with

the proper contrast enhancement techniques. There-

fore, the image’s properties, such as contrast change,

artifacts, and over enhancement, are considered when

comparing the image’s perceived efﬁciency.

The gradient-based contrast enhancement ap-

proach discussed in Section 2 is tested on CT Covid

image database (Zhao et al., 2020) which consists

of 349 Covid and 397 non-Covid CT images col-

lected from 216 patients. The efﬁcacy of the GCE

algorithm is analysed and compared to the methods

RESE (2016), CEF (2018), EEBHE (2019), and JHE

(2019) using qualitative and quantitative research.

The qualitative analysis focuses on visual inspection,

which provides information on annoyances, irregular

enhancement, and over enhancement. Contrast im-

provement index (CII) for quantifying the local con-

trast improvement, discrete entropy (DE) for measur-

ing the information details, Kullback-Leibler distance

(KL-Distance) for measuring the uniform distribution

are some of the output metrics used in quantitative re-

search. The qualitative and quantitative analyses, as

well as the ﬁndings, have been addressed in this sec-

tion.

3.1 Visual Analysis

The qualitative analysis focuses on visual inspec-

tion, providing information on annoyances, irregu-

lar enhancement, and over enhancement. Figure 2

shows some examples of images taken from the Covid

dataset and their histograms. The intensity levels are

spread in the histogram of the sample images in a

small area in the complex grayscale with an irregular

spread. The pixel intensities occupy a narrow interval

in the entire grayscale. It creates a minimal differ-

ence between the various objects in the image, which

results in low contrast.

Figure 3 to Figure 4 display the improved images

obtained by different methods. For the sample image,

Figure 3 displays the contrast improved images and

their respective histograms obtained through various

methods of the sample image

The enhanced images produced by the RESE tech-

nique are shown in Figure 3(a) and the ﬁrst column of

Figure 4. RESE produces an improved image with

less perceived contrast in clarity, as seen in the ﬁg-

ures. The edge information is retained after process-

ing, but the histogram indicates that the improved im-

age has the same range of pixel intensities as the im-

age input, leading to minor contrast improvement than

other techniques. Figure 3(b) and the second column

of Figure 4 depicts the enhanced images of CEF. The

local regions of images have been improved in these

ﬁgures. But, the presence of histogram pits produces

artifacts near the edges.

The EEBHE method has been proposed to im-

prove edge information. Due to the directed ﬁltering

used in the EEBHE process, it is apparent from the

Figure 3(c) and third column of Figure 4 that EEBHE

produces enhanced images with improved edge de-

tails. However, since the resulting images use the full

grayscale dynamic range with uneven distribution, it

can result in minor contrast enhancement in the en-

hanced image’s local area.

JHE provides a high contrast picture, which can

SIGMAP 2021 - 18th International Conference on Signal Processing and Multimedia Applications

(a) (b) (c) (d) (e) (f)

(g) (h) (i) (j) (k) (l)

Figure 2: Sample images: (a) I1, (b) I2, (c) I3, (d) I4, (e) I5, (f) I6, (g) histogram of I1, (h) histogram of I2, (i) histogram of

I3, (j) histogram of I4, (k) histogram of I5, and (l) histogram of I6.

(a) (b) (c) (d) (e)

(f) (g) (h) (i) (j)

Figure 3: Contrast enhanced images of I1: (a) RESE, (b) CEF, (c) EEBHE, (d) JHE, (e) GCE, (f) histogram of RESE, (g)

histogram of CEF, (h) histogram of EEBHE, (i) histogram of JHE, and (j) histogram of GCE.

be seen from Figure 3(d) and the fourth column of

Figure 4. It is because it makes use of the entire com-

plex grayscale range. However, due to average spatial

neighbourhood information in the transformation, the

intensities are not evenly distributed, and the resultant

image is smoothed.

Figure 3(e) and the ﬁfth column of Figure 4 dis-

play the enhanced images resulted from the GCE

technique. Due to the use of multi-scale analysis and

a dark pass ﬁlter, the GCE approach produces an im-

age with increased contrast and no loss of informa-

tion data. The edge information is retained in the en-

hanced image. It can be seen in the enhanced im-

ages’ artifact-free edges. In comparison to the meth-

ods available in the literature, the GCE technique, ac-

cording to the qualitative review, produces enhanced

and artifact-free images.

3.2 Quantitative Analysis

Qualitative analysis resolves the potential of the en-

hancement methodology that human eyes justify.

Quantitative analysis may be used to quantify the efﬁ-

cacy of the enhancement algorithms. A performance

indicator accurately and automatically estimates an

image’s consistency. A perfect objective measure

should be able to represent the subjective measure’s

quality predictions.

3.2.1 Contrast Improvement Index (CII)

It is possible to calculate the local contrast using CII

between the input and output images. (Vijayalakshmi

and Nath, 2021b; Zeng et al., 2004)

CII =

M (C

loc

(J))

M (C

loc

(I))

(12)

where

loc

max − min

max + min

(13)

where max and min represent the high and low inten-

sity values in a 3×3 window respectively. Higher CII

indicates better contrast improved image.

3.2.2 Discrete Entropy

Discrete entropy measures the degree of randomness

and the amount of visible information present in the

Novel Pre-processing Stage for Classiﬁcation of CT Scan Covid-19 Images

Figure 4: Contrast enhanced images obtained by various methods. First column: RESE; second column: CEF; third column:

EEBHE; fourth column: JHE; and ﬁfth column: GCE.

image (Shannon, 1948). A greater entropy value de-

ﬁnes good information for the image. It is determined

by:

E(I) = −

∑

l=1

p(i

)log

p(i

) (14)

where p(i

) is the probability of the pixel value i

. P

indicates the total number of gray values.

3.2.3 KL-Distance

The ﬂatness of the intensity spread in the contrast im-

proved image is measured by the difference between

the enhanced image’s gray level distribution and

the uniform distribution (Vijayalakshmi and Nath,

2021a). It is calculated using the Kullback-Leibler

(KL) distance, as shown in equation (15). The lower

KL-distance represents a uniform spread of pixel in-

tensities.

KL(p,q) =

∑

∀k

p(y

)log



p(y

)

q(y

)



(15)

where p (y

) and q (y

) denote the spread of the con-

trast improved image and uniform distribution, re-

spectively.

Table 1: CII values of contrast enhancement technique.

Methods/

Images

RESE CEF EEBHE JHE GCE

I1 1.3 2.83 1.84 2.89 3.03

I2 1.7 2.5 1.23 2.8 2.99

I3 1.9 2.6 1.15 2.6 2.94

I4 1.13 1.87 1.69 1.6 1.96

I5 1.02 1.96 1.67 2.01 2.19

I6 1.00 1.97 1.71 1.96 2.10

Table 2: DE values of contrast enhancement technique.

Methods/

Images

RESE CEF EEBHE JHE GCE

I1 7.01 6.8 7.21 7.93 7.96

I2 7.10 6.84 7.17 7.8 7.97

I3 7.04 6.83 7.12 7.88 7.95

I4 7.24 7.02 7.37 7.70 7.98

I5 6.92 6.72 6.97 7.65 7.97

I6 6.90 6.85 7.2 7.67 7.94

All of the sample images’ CII metric values are

mentioned in Table 1. It shows that GCE produces

high CII values as compared to the other approaches.

This is because the GCE approach uses neighbour-

SIGMAP 2021 - 18th International Conference on Signal Processing and Multimedia Applications

Table 3: KL values of contrast enhancement technique.

Methods/

Images

RESE CEF EEBHE JHE GCE

I1 0.08 0.07 0.66 0.05 0.028

I2 0.04 0.05 0.17 0.06 0.02

I3 0.09 0.08 0.44 0.08 0.04

I4 0.28 0.66 0.24 0.27 0.17

I5 0.92 0.56 0.59 0.34 0.146

I6 0.93 0.46 0.42 0.53 0.053

Table 4: Average metric values for various methods of

Covid database.

Methods/

Metrics

RESE CEF EEBHE JHE GCE

CII 1.15 1.82 1.48 2.07 2.14

DE 6.24 6.07 6.33 7.16 7.27

KL 0.3 0.12 0.2 0.15 0.05

hood details in the mapping function to help increase

the image’s contrast in the surrounding area. As seen

in the qualitative analysis, the entities are differenti-

ated due to increasing contrast in the small areas.

Table 2 displays the DE values for the sample im-

ages. The GCE method results in a higher entropy

value than other related methods, as observed. It is

due to the use of edge information in the discrete func-

tion formulation.

Table 3 shows the KL-distance of different en-

hancement techniques. In comparison to the other

methods, these results indicate that the proposed ap-

proach distributes intensities equally. Furthermore, it

demonstrates that the GCE algorithm generates an im-

proved image with high contrast in the absence of his-

togram spikes.

The GCE algorithm and methods outlined in the

literature were tested on the entire database to im-

prove the accuracy of the evaluation. For the whole

database, the average values of the output metrics are

tabulated in Table 4. The Table shows that the GCE

algorithm improves the contrast while preserving the

information details with uniform distribution of inten-

sity values compared to the methods discussed in the

literature.

Table 5: Classiﬁcation accuracy values for Covid database.

Methods

Accuracy

(in %)

Unprocessed 73.32

RESE 75

CEF 74

EEBHE 76.04

JHE 78.72

GCE 80.6

3.2.4 Assessment of GCE in Machine

Intelligence

A pre-trained AlexNet is used to investigate the ef-

ﬁciency of a gradient-based contrast enhancement al-

gorithm in the ﬁeld of machine intelligence. For covid

detection, the CT scan Covid and non-Covid images

are used.

The assessment is carried out in the following two

phases. In the ﬁrst phase, the AlexNet is trained and

tested with the images without enhancement. For

training, 80% of Covid images and non-Covid images

are provided to the network. The remaining 20% of

images from the two classes are tested. As a result,

the network offers classiﬁcation accuracy of 73% in

images without enhancement.

In the second phase, the network is trained and

tested with 80% and 20% of the pre-processed im-

ages, respectively. The contrast enhancement algo-

rithms discussed in the literature and the gradient-

based contrast enhancement method are used as a pre-

processing stage. Table 5 shows the classiﬁcation ac-

curacy of unprocessed and enhanced Covid-19 im-

ages of various methods. It is inferred from Table

5 that enhanced images help in improving the clas-

siﬁcation accuracy. It is observed that with GCE, the

classiﬁcation accuracy is 80.62%, which is the high-

est value when compared to the other techniques dis-

cussed in the literature. Therefore, it may be con-

cluded that the GCE algorithm aids in the improve-

ment of classiﬁcation accuracy of CT scan Covid-19

images.

4 CONCLUSIONS

In this paper, a pre-processing stage for improving

the classiﬁcation accuracy of Covid-19 CT scan im-

ages is described. It uses gradient-based contrast

enhancement (GCE) as a pre-processing stage. In

GCE, the mapping function uses the joint distribution

of edge information and intensity values to map the

pixel values to ﬁll the complete grayscale with a uni-

form spread. It has been shown that the method can

increase contrast by reducing histogram peaks and

pits, resulting in artifact-free contrast improved im-

ages. The method outperforms increasing contrast,

avoiding loss of information, and ensuring a consis-

tent distribution of gray levels, which can be seen in

the histogram and measured using the KL-distance.

Furthermore, a pre-trained AlexNet is used to investi-

gate the efﬁcacy of a gradient-based contrast enhance-

ment algorithm. After increasing the contrast of the

images using GCE, the classiﬁcation accuracy is im-

Novel Pre-processing Stage for Classiﬁcation of CT Scan Covid-19 Images

proved from 73.32% to 80.62%, according to the re-

sults. Hence, it may be concluded that the GCE al-

gorithm can be used as a pre-processing stage for im-

proving the classiﬁcation accuracy of CT scan Covid-

19 images.

ACKNOWLEDGEMENTS

The work has been supported by the department of

ECE, National Institute of Technology Puducherry,

India.

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