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Sensors and Materials, Vol. 35, No. 10 (2023) 4633–4652
MYU Tokyo
S & M 3419

Fully Automated Construction of a Deep U-Net Network Modelfor Medical Image Segmentation
Daoqing Gong,1 Jiayan Yang,1 Xinyan Gan,1* Xiang Gao,1 and Yuanxia Zhang2*
1School of Public Health and Management, Guangxi University of Chinese Medicine, Nanning 530299, China
2School of Computer and Information Engineering, Yulin Normal University, Nanning 537000, China(Received July 14, 2023; accepted October 4, 2023)

Keywords: fully automatic, gene expression programming, U-Net Model, medical images

In recent years, the use of deep learning technology for image processing has become
mainstream, and the U-Net network has received widespread attention owing to its unique
U-shaped structure, which has achieved excellent results in the field of image segmentation,
especially in medical image segmentation. To enhance the performance of the U-Net networkmodel and establish better U-Net design variables, in this paper, we propose a fuzzy-controlled
multicellular gene expression programming algorithm to automatically build and optimize the
U-Net. The algorithm creates an efficient variable-length gene code, generates chromosomes forthe optimization of U-Net design variables, decodes the chromosomes to construct the U-Net
model, dynamically calculates population fitness and fuzzy affiliation values, and achieves the
optimal U-Net network through continuous evolution. The experimental results indicate that the
proposed algorithm outperforms U-Net, Fully Convolutional Networks32, and VanillaUnet inimage recognition segmentation, especially in the segmentation of COVID-19 CT medical
images.

1.

Introduction

Computer vision is a field that enables computers to understand and process visual data, suchas images and videos. With the continuous development of society and the application of deeplearning techniques, advancements in computer CPUs and GPUs have significantly improved
the efficiency and accuracy of computer vision tasks. Deep learning techniques leverage large
amounts of data to train neural networks, enabling them to better recognize and analyze media
information data, including images and videos, through continuous learning.
In the field of computer vision, semantic segmentation and image classification havebecome integral parts of our daily lives. For instance, face recognition (1) utilizes semantic
segmentation to identify specific parts of a person’s face, while autonomous driving (2,3)
requires the real-time monitoring of various types of semantic information on the road to
avoid accidents. Medical image segmentation (4) plays a crucial role in accurately*Corresponding author: e-mail: 18775300556@163.com

https://doi.org/10.18494/SAM4587

ISSN 0914-4935 © MYU K.K.
https://myukk.org/

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Sensors and Materials, Vol. 35, No. 10 (2023)

identifying disease-specific areas at the pixel level, assisting doctors in formulating
treatment plans for patients.
Over the years, extensive research and development efforts have yielded impressive
results in both semantic segmentation and image classification.(5) Deep learning models
such as Alex Net (6) have achieved remarkable classification accuracy on ImageNet
datasets by employing multilayer convolutional neural networks. The ResNet model(7)addresses the degradation problem of deep residual networks by increasing network
depth. Other models, including DenseNet,(8) EfficientNet,(9) MobileNet,(10) Shuff leNet,(11)and SegNet,(12) have also demonstrated excellent performance across various tasks. Theseadvancements in deep learning have been made possible by the availability of highquality labeled datasets and the exponential growth of computer computing power.(13)These models have significantly contributed to the progress and success of computer
vision tasks, enabling computers to better understand and interpret visual data.
Semantic segmentation methods based on deep learning have demonstrated state-ofthe-art performance in tasks such as medical image classification, detection, andsegmentation.(14) A survey conducted by Li et al.(15) revealed that segmentation is themost sought-after task in medical image analysis.(16) Since 2015, the utilization of U-Net
models (17) in medical images has gained significant momentum.(18) The U-Net model has
become a popular research focus in this field owing to several advantages, including hightraining speed, the ability to achieve good results with limited annotated data, theefficient utilization of contextual information, feature learning from local and global
aspects, and high transferability. However, the application of U-Net in medical images
presents challenges due to privacy concerns associated with patient data. Medical images
are characterized by high noise, variability, large scale, and limited training samples,making image processing and analysis highly challenging. Among various approaches,
deep-learning-based semantic segmentation methods have demonstrated superior
performance in addressing medical image problems. The U-Net model, being one of thesemantic segmentation methods, effectively tackles the challenges encountered in medical
image tasks and serves as a powerful tool for medical image processing and segmentation.
(19,20) Its key applications include the automatic multiclass segmentation of COVID-19
chest CT images,(21) the automatic segmentation of rectal cancer regions,(22) and thesegmentation of liver tumors and major blood vessels.(23)
Optimizing convolutional neural networks (CNNs) is crucial for refining the overall
algorithm and reducing unnecessary resource and time waste. However, network
optimization requires expertise and extensive knowledge in the field.(24) It is common for
network optimization to encounter challenges such as getting trapped in local optima
while searching for the global optimal solution. Training time can be long, especiallywhen dealing with large datasets. Moreover, network optimization often requires
substantial computer memory and computational power. The random initialization of
network parameters typically necessitates training through numerous iterations to findthe best approach, often referred to as the “brute force method”.(25) Considering thesechallenges, many experts and scholars have proposed various optimization methods.

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These include grid search,(26) random search (RS),(27) Bayesian-based optimization(BGP),(28,29) Parzen estimator (TPE) for tree networks,(30) global optimization (SMBO)
based on sequential models, (31) neural evolution with extended topology, (32) andevolutionary unsupervised deep learning.(33) These methods aim to overcome thelimitations and improve the efficiency of network optimization by utilizing different
strategies and algorithms. Overall, network optimization plays a critical role in enhancing
CNN performance, but it requires specialized knowledge and expertise. Researchers havedeveloped several optimization methods to address the challenges associated with finding
the best solutions and improving efficiency in training deep neural networks.
Gene expression programming (GEP), mentioned in this paper, is an evolutionary
computational method based on genetic algorithms,(34) which can be applied to a wide range ofproblems and has good flexibility and adaptability. Through GEP, multicellular gene expression
programming can be further extended in terms of its expressive power and search space.
Evolutionary algorithms have proven to be one of the most competitive methods for neuralnetwork optimization problems,(35) and there are a large number of relevant parameters(36) that
require human intervention to find a good U-Net network model.(37)In this paper, we propose a fuzzy-control-based multicellular gene expression
programming algorithm to automatically build and optimize U-Net. It was also applied on
medical image data of colorectal cancer images and COVID-19 CT images for validation,and compared with classical algorithms and other advanced algorithms. The results showthat the algorithm proposed in this paper achieves a better fully automated segmentation
effect in image segmentation. The main contributions of this paper are summarized asfollows:(1)	Efficient Model Building: The proposed algorithm introduces a flexible coding strategy that
automates the construction of a deep U-Net network model. This coding strategy significantly
reduces the consumption of human, material, and financial resources involved in model
development. By automating the process, the algorithm streamlines the model building
workflow, leading to improved efficiency and cost-effectiveness.
(2)	
Scalability and Adaptability: The algorithm’s design is not limited to specific U-Net
constructs or segmentation tasks. It can be applied to various segmentation tasks, allowing
for the development of models with different architectural configurations. This flexibility
enables researchers and practitioners to adapt the algorithm to specific requirements andachieve optimal performance in diverse applications. Additionally, the algorithm addresses
the challenges associated with framework construction, eliminating the need for manualadjustments of parameters. This eliminates the tediousness of parameter tuning and enhances
accuracy and other aspects of the U-Net model.
(3)	Performance Improvement: By automating the U-Net model construction and optimization
process, the algorithm contributes to improved performance in image segmentation tasks.
The efficient model building and adaptable nature of the algorithm allow for a better
utilization of resources and enable researchers to focus on other critical aspects of thesegmentation problem. Consequently, the algorithm aids in achieving higher accuracy andtraining speed, and improved overall results in image segmentation tasks, particularly in the
medical imaging domain.

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2.

Preliminaries

2.1

Fuzzy control multicell gene expression programming

GEP is stochastic search and optimization adaptive evolutionary algorithm(34) that inherits
the fixed length of genetic algorithms (GAs) and the flexible and versatile tree structure ofgenetic programming (GP). Unlike GP, GEP uses a linear chromosome rather than a tree
structure to represent functions and can generate functions of arbitrary size and complexity.
This gives GEP greater flexibility and scalability in solving real-world problems. GEP is uniquein that it uses a gene expression approach, making the code cleaner and more readable, but
owing to its iterative nature, it requires a large amount of computation especially when dealing
with large-scale problems.(38) Multicellular gene expression programming is based on GEP byintroducing the concept of homologous genes, which enhances the search and expression
capabilities of GEP.(39) Fuzzy control is an intelligent control method based on fuzzy set theory,
fuzzy linguistic variables, and fuzzy logical reasoning.(40) Fuzzy control multicellular gene
expression programming is the introduction of fuzzy control based on multicellular gene
expression programming, abbreviated as FMCGEP.
GEP utilizes chromosomes to represent individuals, which consist of two or more gene
expressions. Gene expressions are linear strings of symbols with fixed lengths, containing both
non-terminator and terminator nodes. An individual in GEP comprises both common andhomologous gene types. Chromosomes are composed of multiple fixed-length, equally sized
genes, and the initial population is formed by randomly generated chromosomes. Genetic
operators such as mutation, string insertion, single-point recombination, two-point
recombination, and genetic recombination(41) ensure the legality of gene structures and theintegrity of chromosome structures. These operators play a crucial role in producing new
individuals by modifying gene expressions. Tournaments are used to select individuals with thehighest fitness values for reproduction, allowing for the evolution of the population in a
meritocratic manner. Through this iterative process, the population gradually adapts to theenvironment, eventually converging towards the optimal solution.
The structure of GEP can be divided into three parts, which are the head h, tail t, and DC
domain. The elements that make up each part are shown in Table 1. Different genes can beconnected to each other with function characters, and the relationship between the head and thetail can be expressed ast = h × (n − 1) + 1.
Here, n represents the maximum number of arguments allowed by the operator.
Table 1
Genetic building blocks.
Position
Constituent elementHead+, −, *, /, %, sin, cos, tan, max, min, and, or, not,?,a,b,c,d,e,f,g,h,iTail?,a,b,c,d,e,f,g,h,i
DC domain
1,2,3,4,5,6,7,8,9,10(1)

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Gen1 and Gen2 in Fig. 1 represent common gene 1 and common gene 2, respectively,
where “?” in the tail of Gen1 corresponds to the C letter in the DC domain of Gen1, whichmaps to the third number in the Constants set (1.7601). the homeotic gene denotes ahomologous gene, and 1 in the tail denotes Gen1 and 2 denotes Gen2. The decoding of
MCGEP in Fig. 1 yields the expression tree network shown in Fig. 2. The formula obtained after
decoding the MCGEP tree network according to Fig. 2 is shown as(b + ?) * ( c / b ) * (b / c + ?) .

(2)

The FMCGEP algorithm utilizes a fitness function to evaluate the chromosomes’
fitness in the population relative to the environment.(42) This function measures thedisparity between the actual and target values. After calculating the fitness value andfuzzy affiliation of the population, FMCGEP automatically adjusts the crossover rate,variance rate, and fixed set variance based on the fuzzy affiliation. This adaptive
adjustment helps the population escape local optima and converge rapidly. To enhance thepopulation’s diversity, FMCGEP employs genetic operations that introduce randomness
and generate new chromosomes through genetic operators. These newly generated
chromosomes are added to the population, allowing for a more comprehensive explorationof the search space and the discovery of improved solutions.(43)

Fig. 1.

Fig. 2.

MCGEP genotype network.

MCGEP tree network.

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2.2

Sensors and Materials, Vol. 35, No. 10 (2023)

U-Net model

The U-Net model is a pixel-level segmentation model that has been widely used in medical
imaging for lesion segmentation tasks and has become an effective aid to physicians indiagnosing diseases. The structure of the U-Net is shown in Fig. 3.
The U-Net model follows a systematic process involving an encoder and a decoder to perform
image segmentation. The input image is first passed through the encoder, which consists ofmultiple convolution and pooling operations. The convolution layers employ a 3 × 3 unfilled
convolution kernel with a stride of 1 and a ReLU activation function. The pooling layers use a 2
× 2 maximum pooling technique for down-sampling. The role of the encoder is to transform theoriginal image’s low-level information into high-level, abstract feature representations with
reduced dimensionality and increased channel depth. The decoder, on the other hand, performs
multiple transpose convolution and up-sampling operations. The original image, which wastrained by the encoder, serves as input to the decoder. The up-sampling stage employs
deconvolution operations to restore the image resolution to its original size. Additionally, thefeature maps from the up-sampling process are merged with the corresponding encoder layer’s
feature maps using skip connections. This merging process aids in reconstructing the original
image by preserving important information from earlier stages of the network.

Fig. 3.

(Color online) Structure of U-Net network model.

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Lastly, two additional 3 × 3 convolution layers with a ReLU activation function are employedto accurately recover image details while preserving global features. One challenge inconvolution is the loss of image edge information due to the absence of padding. To address this,an overlap-tile strategy that applies mirror expansion to the pixels surrounding the medical
image was introduced.(17) This approach helps infer missing context, better preserves global
information, and handles images of various sizes. After passing through the convolutional
layers, a 1 × 1 convolutional kernel (without the ReLU activation function) is applied, and thefinal segmentation result is obtained. To further enhance segmentation quality, a weighted loss
is utilized to assign higher importance to specific pixel points. The U-Net network consists of 18
convolutional layers. To prevent overfitting, the training dataset is augmented by incorporatingtransformations such as rotation, flipping, and translation.

3.

Proposed Algorithm

3.1

Algorithm overview

Algorithm 1 outlines the framework of the FMCGEP-U-Net algorithm and is divided into
five main sections: Inputs: population size N, maximum number of iterations G, crossover rate
Pc, variation rate Pm, and tournament size Pt. Output: optimal U-Net model.
(1)	The population is first initialized according to the proposed FMCGEP coding strategy, andthe design variables to be optimized for the U-Net are coded into the chromosomes during
the random initialization of the population (see line 1 of the code).
(2)	The chromosomes are decoded to construct the U-Net model and evaluated, and the resulting
evaluated values are used as individual fitness values (line 2).
Algorithm 1
FMCGEP-U-Net framework.
Input:
Population size N, Maximum number of iterations G, Crossover rate Pc, Variation rate Pm, Tournament
size Pt.
Output: Optimal U-Net model.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18

P0 ←Initialize a population with the size of N by MCGEP coding strategy;
Construct the U-Net model, evaluate the fitness of individuals in P0;
Sort P0;
Calculated membership (degree);
While t<G do
Qt ← Ø
While Qt < N do
If degree < 0.6 or degree > 1
Pm = Pm, Pc = Pc;
else: Pm = Mutation_fuzzy_control, Pc =Cross_fuzzy_control;
q1, q2 ←do mutation operation, do crossover operation;q3 ←do the mutation first then the crossover operation;
q4 ← do the crossover first then the mutation operation;
Qt ←q1Սq2Սq3Սq4;
End
Sort Qt, which population size equal to N;
End
Select the best U-Net from Qt.

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Sensors and Materials, Vol. 35, No. 10 (2023)(3)	Comprehensive ranking of individual fitness values (row 3).
(4)	The adaptation variance is used as a parameter for fuzzy control and the fuzzy affiliation is
calculated (row 4).
(5)	Finally, the optimal U-Net network model is obtained by continuous evolutionary iteration(line 18).
During the execution of the code (lines 5–17), a new population Qt of size N needs to becreated. If the affiliation of an individual is less than 0.6 or greater than 1, the mutation rate Pm
and the crossover rate Pc remain unchanged; otherwise, fuzzy logic control is used to adjust theprobability parameters of mutation, crossover, and constant mutation according to the fitness
value of the current population. Genetic operations were carried out on the population, q1, q2, q3
and q4 represented four temporary populations, and then genetic operations of mutation,
crossover, post-mutation crossover, and post-cross mutation were carried out on the temporary
population. The offspring produced by all four genetic operations are collected into the parental
population (Qt) and ranked according to fitness values, and the best U-Net model is selectedfrom Qt at the end of each iteration.
The process of medical image identification is implemented according to our algorithm,
as presented in Fig. 4. In the first step, we used the FMCGEP algorithm to train theparameters (e.g., convolution and activation function) of the U-Net model to be constructed,
and we used the optimized parameters for the construction of the U-Net model. The two
medical image datasets, COVID-19 and colorectal carcinomatosis, were trained using theparameter-optimized model, and then the results of this training were derived from theobtained results and the generated medical image segmentation effect map, through a fully
connected layer, with the final output of the probability of image classification.

Fig. 4.

(Color online) FMCGEP-U-Net algorithm implementation framework.

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3.2

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Genetic coding strategy

According to Sect. 2.2, the U-Net model consists of two types of operation: convolutional andpooling layers. Additionally, jump connections directly connect certain features from theencoder to the decoder. The optimal depth of the U-Net model structure is task-dependent, andthe depth size impacts the model’s performance. A deeper model can capture higher-levelfeatures from the image information, but it also requires more computational resources and time.
Therefore, the proposed FMCGEP-U-Net genetic encoding strategy in this paper can be adapted
to the specific requirements of practical problems to enhance its suitability.
In the genetic algorithm, each gene represents the parameters needed in the U-Net model.
The gene length is determined by the combined lengths of the head, tail, and DC domains.
Initially, a sequence of strings representing the gene is randomly generated from a set ofoperators and constant variables, based on the predefined parameter settings, by traversing thegene length.
3.3

FMCGEP-U-Net network encoding

There exists a significant correlation between the performance of medical image
segmentation and learning parameters. Typically, the segmentation performance of a model iscompared by adjusting parameters such as the activation function and pooling size.(44–46) In the
FMCFGEP-U-Net strategy, gene encoding is used to represent the values of each hyperparameterin the U-Net network structure. Each hyperparameter is encoded into a single gene to express its
value. The generated genome is then synthesized into the chromosome of the U-Net design
variable for optimization, following the chromosome structure shown in Fig. 5. It is crucial toverify whether the generated chromosome adheres to the defined domain. This verification canbe achieved by decoding each gene and calculating its value representation to perform a range
check. If a gene value falls outside the defined domain, it is regenerated until it meets the defined
criteria.

Fig. 5.

(Color online) MCGEP chromosome network.

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Assuming that there are M design variables to be optimized in the chromosome, they can be
represented by M homologous genes, which are obtained by mapping N common genes, which
are decoded into a specific column of real numbers. We extract the genetic parameters from theconforming chromosomes, which include the activation function, optimizer, convolution andpooling kernel, pooling type, dropout rate, learning rate, L (regularization parameter), batch
size, number of epochs, number of convolution blocks, number of residual blocks, number ofhidden layers, and number of neurons in each hidden layer, and use these genetic parameters toconstruct the U-Net network model with the specified optimizer to compile the model. To verify
how good the model is, we train the model using the dataset, calculate the training data loss andmetrics for the model, and save the model locally.
The activation function, optimization method, convolution kernel size, pooling kernel size,and pooling type in the homologous genes in Fig. 5 are represented by the corresponding
Hgen1–Hgen6, Hgen7 indicates the number of network layers, and Hgen8–Hgen11 corresponds
to the number of neurons in the hidden layer.
3.4

Population initialization

Algorithm 2 gives the main steps of population initialization. For the initialization of a
population to be implemented, it can be broken down into a given minimum number of genetic
units, which in turn form chromosomes from multiple genes and then individuals fromchromosomes, with multiple individuals gathered to form a population. In this algorithm, three
input parameters are accepted, the population size N, the number of chromosomal genes Gn, andthe chromosome length CL, and an initialized population P0 is output. First, P0 and F0 are
generated giving an initial value of null; for each individual, an empty list G0 is first initialized,a specified number of genes are generated by the statement, the genes that match are constructed
into chromosomes, and the generated chromosomes are added to P0. On the basis of thegenerated chromosomes, the model is evaluated for health using the specified optimization
function, and the fitness value of the chromosome can be obtained and stored in F0.
Algorithm 2
Population initialization.
Input:
 opulation size N, number of chromosome genes Gn, chromosome length CL.
P
Output: Initialized population P0.
1 P0 ← Ø, F0 ← Ø, i ← 1;
2 For i ≤ N do
3
G0 ← Ø, j ← 1;
4
For j ≤ Gn do
5
G0 ←Generate Gn gene sequences
6ɑ←1
7
If ɑ ≤ CL then8
P0 ←Generate a chromosome and determine whether each gene in G0 matches the defined domain. If not,the gene will be regenerated.
9
F0 ←Calculate chromosome fitness value
10 End11 Return P0

Sensors and Materials, Vol. 35, No. 10 (2023)

3.5

4643

Fitness evaluation

Algorithm 3 describes the health assessment process. In the evolutionary algorithm, fitness
values are assessed and used to determine which of the best individuals have the ability toreproduce offspring. An evolutionary operation is performed on each individual in the
population; the parameters in the chromosome are used to construct a U-Net model; the U-Net
model is trained with a training dataset and the accuracy on the test dataset is stored in the set
Dfitness as the fitness of that individual; the model evaluation value is used as the individual
fitness value and the optimal fitness value of the population is obtained by continuous evolution.
Algorithm 3 describes the health assessment process. In the evolutionary algorithm, fitness
values are assessed and used to determine which of the best individuals have the ability toreproduce offspring. An evolutionary operation is performed on each individual in the
population; the parameters in the chromosome are used to construct a U-Net model; the U-Net
model is trained with a training dataset and the accuracy on the test dataset is stored in the set
Dfitness as the fitness of that individual; the model evaluation value is used as the individual
fitness value and the optimal fitness value of the population is obtained by continuous evolution.
The fitness evaluation assesses the quality of the population by measuring the fitness of eachchromosome within the environment. The optimal individual is then selected as the genetic
parent for the next generation of the population, and the design variables of the U-Net are further
optimized to continue its evolution. The ultimate objective of this process is to achieve an
optimal U-Net model. A variable-length genetic encoding strategy is employed, allowing each

Algorithm 3
Fitness evaluation.
Input:

Initialize population Pt, train data Dtrain, test data Dtest, the training epoch number ƙ for measuring theaccuracy tendency.
Output: Optimal fitness value Ftrain
1 For each individual in Pt do
2 i ← 1;
3 while i ≤ ƙ do
4
Train the connection weights represented by individual S;
5if i==ƙ then6
accy_list ← Ø, Dfitness ← Ø;
7
U-Net ←
 Build a model by using the parameter values in chromosomes as parameters for modeltraining；
8
Train U-Net model on Dtrain;9accy_list ← Evaluate the classification accuracy of the trained U-Net on Dtest
10
Dfitness ← Assign accuracy as the fitness of individual;
11End
12 End13 Sort the elements in Dfitness;
14 Bestvalue ← The optimal fitness value of the current population was obtained;
15 Degree ←Calculate population diversity (mean/maximum);16 Dynamically adjust parameters based on degree;
17 Update S in Pt;
18 End19 Return Pt

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individual to be initialized with a small number of epochs for training, based on their architecture
and weights. The fitness value of each epoch is calculated, along with metrics such as varianceand fuzzy affiliation, to evolve the regulatory population. The evaluation metrics used in thisstudy include accuracy, Dice coefficient, Jaccard index, and loss. These metrics are employed toassess the performance and effectiveness of the proposed approach.
The selection of an appropriate fitness function is crucial for enhancing the algorithm
performance. It is essential to conduct a thorough analysis and choose the most suitable fitness
function for specific tasks or even design a custom fitness function based on the specificsituation.
3.6

Environmental selection

Algorithm 4 outlines the process of tournament selection, which involves carefully selecting
individuals from the population through environmental selection. Firstly, the algorithm
determines the number of individuals to be selected on the basis of the population size (line 1).
Next, n individuals are randomly chosen from the entire population, and their fitness values arecompared. The individuals with higher fitness values are then selected to form the next
generation of the population (line 2). This process emulates natural selection, where the fittest
individuals survive. The process continues until the specified requirements are met (rows 3–5).
The choice of the value of n significantly affects the overall algorithm. A value that is very large
increases the likelihood of selecting individuals with high fitness values, while a value that isvery small may result in a less effective selection of the best individuals and have a greater
impact on the subsequent generation of populations.
In this paper, we designed two sets of experiments on selected colorectal cancer images and
COVID-19 CT image datasets to test the performance of the algorithm. The datasets to be used
are briefly described below, and the parameter settings for the FMCGEP-U-Net method toparticipate in the experiments are given.

Algorithm 4
Environmental selection.
Input:
Current population Pt Ս Qt.
Output: Select Population Pt+1.
1
2
3
4
5
6
7

n ← Determine the optimal number of individuals selected on the basis of population size N;
Pt+1 ←From the n randomly selected individuals, compare their fitness values and select the best individual;
While | Pt+1 | < N do
S ←Randomly select 20% of the population size N from Pt Ս Qt;
Pt+1 ←Pt+1 Ս S
EndReturn Pt+1.

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4.

Dataset4.1

Dataset description

4645

The task of medical image segmentation is mainly to segment medical images into several
similar or different regions by automatic or semi-automatic methods.(47) Medical image data
information involves patients’ personal information, and according to legal and ethicalconstraints and norms, this image data can only be used by specific people within a defined
scope, which also leads to a more difficult access to medical image data and reflects thepreciousness of publicly available medical image datasets. In this paper, we use the private
COVID-19CT and colorectal cancer datasets, the source data of which is the publicly available
COVID-19CT dataset, and manually select the better image composition in order to better train
the model and reduce the risk of overfitting. Regarding the images and CT_NonCOVID files, the
COVID-CT-Images file contains 264 CT images of newly crowned patients and the CT_
NonCOVID file contains 1000 CT images of non-newly crowned patients (a total of 1264
images).
The colorectal cancer image dataset contains colorectal histology images of eight diseasetypes. The eight different disease categories are represented by the eight folders 01_TUMOR,
02_STROMA, 03_COMPLEX, 04_LYMPHO, 05_DEBRIS, 06_MUCOSA, 07_ADIPOSE, and
08_EMPTY. Each folder contains 625 images, each with a size of 150 × 150, a pixel density of
72 dpi, and 24-bit depth, for a total of 5000 images of colorectal cancer. We randomly assigned
80% of each dataset as the training set and 20% as the validation set by breaking up each dataset,and the details of the number of images used in each dataset are shown in Table 2.
4.2

Parameter settings

The FMCGEP parameters include gene length, head and tail length, population size, thenumber of evolutionary iterations, and so forth. The U-Net parameters include upper and lower
limits and types of convolutional kernel size, upper and lower limits and types of pooling kernel,
and so forth. The detailed parameters of the algorithm are shown in Table 3.

5.

Experimental Results and Analysis

We set up two sets of experiments to train and compare the FMCGEP-U-Net algorithm andthe U-Net algorithm on the COVID-19 image and colorectal cancer image datasets, so we will
analyze and discuss the results out of these two sets of experiments in detail.
5.1

COVID-19 image experiment

On the COVID-19 image dataset, we conducted comparative analyses mainly in terms ofclassification accuracy, loss function values, Dice similarity coefficients, and Jaccard similarity
coefficients. To calculate these performance measures, confusion matrices were used, which

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Table 2Distribution of images in the dataset.
DatasetCOVID-19CT
Cancer of large intestine
Table 3FMCGEP-U-Net parameter settings.
Parameter
Gene length
Head length
Tail length
DC domain
Population size
Number of evolutionary iterations
Tournament ratio
Crossover rate
Gene length

Sensors and Materials, Vol. 35, No. 10 (2023)

Training set
1011
4000

FMCGEP

U-Net
Lower and upper bounds of convolution kernel size
Lower and upper bounds of pool kernel size
Number of pool kernel types
Type of pool kernel function
Activation function type

Test set
253
1000

Values
18
6
7
5
20
20
0.1
0.3
18
[3,5]
[2,3]
2
5
5

included true positive (TP), false positive (FP), true negative (TN), and false negative (FN)
variables. The results of the comparative analysis provided showed that the FMCGEP-U-Net
model performed better on the COVID-19 image dataset. In terms of classification accuracy andDice and Jaccard similarity coefficients, the FMCGEP-U-Net model obtained higher results than
U-Net on both the training and validation sets. Although the FMCGEP-U-Net model was slightly
lower in terms of loss function values relative to the U-Net model, its fully automatedsegmentation was better. Therefore, it can be concluded that the FMCGEP-U-Net model is moresuitable for the image discrimination of fully automated segmentation effects in the COVID-19
image experiments.
According to the relationship between the accuracy of the training set and the verification set
and the epoch shown in Fig. 6(a), it can be found that when the epoch is 10, the performance of
the U-Net model starts to flatten out and keeps rising steadily. In the whole epoch range of 0 to
20, the U-Net model has a higher accuracy than the FMCGEP-U-Net model. However, at epoch
20, the accuracy of the FMCGEP-U-Net model exceeds that of U-Net and continues to rise. Itshows that the FMCGEP-U-Net model has a stronger generalization ability and a fasterconvergence than the U-Net model. By comparing the algorithms in Table 4, the accuracy of the
FMCGEP-U-Net model is as high as 90.83%, while that of the U-Net model is only 86.24%.
Therefore, the FMCGEP-U-Net model has better classification performance for COVID-19
image dataset classification.
Figures 6(b)–6(d) show the variations of Dice similarity coefficient, Jaccard similarity
coefficient, and loss function values in each epoch of each model. In terms of Dice coefficientSensors and Materials, Vol. 35, No. 10 (2023)

4647

(a)

(b)

(c)

(d)

Fig. 6.

(Color online) Experimental analysis of the COVID-19 dataset.

Table 4Comparison of methods on the COVID-19 dataset.
Methods
Accuracy rate (%)
Epoch
U-Net
86.24
50
87.68
50
FCN32(48)89.62
50
VanillaUnet(49)
FMCGEP-U-Net
90.83
50

and Jaccard index, the training and verification results of FMCGEP-U-Net were better thanthose of U-Net, indicating that FMCGP-U-Net had better target segmentation performance.
However, it can be seen from the loss function diagram that the loss value of FMCGEP-U-Net islower than that of U-Net in both training and verification sets, so the FMCGEP-U-Net model has
a higher robustness than U-Net.
5.2

Colorectal cancer image experimentAccording to the result of analyzing of the experimental results of FMCGEP-U-Net on thelarge intestine cancer dataset in Fig. 7, it can be found that the modified model shows a good
trend of accuracy, Dice coefficient, Jaccard similarity coefficient, and loss function values in the4648

Sensors and Materials, Vol. 35, No. 10 (2023)

Fig. 7.

(a)

(b)

(c)

(d)

(Color online) Experimental analysis of the colorectal carcinoma dataset.

training and test sets. Figure 7(a) shows that model accuracy gradually improves with theincrease in epoch. After the fifth epoch, the accuracy of the verification set exceeded theaccuracy of the training set and maintained a steady upward trend. After the eighth epoch, theaccuracies of the verification and training sets remained in a reliable stable state until the end of
the training. As shown in Figs. 7(b)–7(c), the Dice and Jaccard similarity coefficients of the
FMCGEP-U-Net model on the training set showed good performance, with the highest value
approaching 0.8, indicating that the model was capable of effectively processing the segmentationand prediction of colorectal cancer images.
In addition, according to the input image of the large intestine dataset and the predictive
mask image shown in Fig. 8, the black part represents the result of image segmentation, which ismarked, and the segmentation effect of this model is good. Overall, some aspects of the model
can be further perfected and improved, but according to the experimental data given in thispaper, its excellent prediction effect shows that the model is feasible.

Sensors and Materials, Vol. 35, No. 10 (2023)

Fig. 8.

6.

4649(Color online) Input and predicted mask images of the large intestine cancer dataset.

Conclusions and Future Work

The main objective of this paper is to automatically build and optimize a U-Net model using
a fuzzy-controlled multicell gene expression programming algorithm to address the medical
image discrimination problem. The proposed FMCGEP-U-Net aims to automatically determine
the optimal depth and parameter settings by combining fuzzy control and GEP. This approachenhances the performance of the U-Net model while reducing the human cost involved.
Experimental results demonstrate that FMCGEP-U-Net achieves superior segmentation results
when applied to COVID-19 CT images and colorectal cancer image data. However, it isimportant to further explore the training results using larger datasets, as the current study
employed a small dataset.
Medical images present unique challenges, including high latitude, large data volume, andcategory imbalance. Overcoming these challenges would greatly benefit model training andperformance evaluation. If we can effectively address the problem of medical image acquisition,
we can obtain high-quality data images, enhance model accuracy, reduce data bias, and increase
the overall application value. Therefore, solving the issue of medical image acquisition would
have a positive impact on the medical field.
Furthermore, it is worth exploring different types of neural network structure, such as segNet
and DenseNet, to address specific image-related problems in various domains. Additionally,
future research should focus on using more complex neural network models to optimize networkarchitectures. There are still numerous possibilities for the application of deep learning, such as
resolving data privacy concerns, improving the protection of personal information, andpotentially achieving qualitative improvements in training time. Future research efforts need tothoroughly investigate these issues and propose effective solutions to unleash the full potentialof deep learning in diverse fields.

Acknowledgments
This work was supported by the university-level scientific research projects of Guangxi
University of Chinese Medicine #2020MS006, Guangxi University of Chinese Medicine-4650

Sensors and Materials, Vol. 35, No. 10 (2023)

Shenzhen Kangtai Biological Products Co., Ltd. Guangxi hepatitis B prevention and control
project #QT022019, and Research on Data Storage Technology for Online Shops in the
Traditional Chinese Medicine Industry #2012YJZX04. Xinyan Gan and Yuanxia Zhang
contributed equally to this work and should be considered corresponding authors.

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About the Authors
Daoqing Gong received his B.S. degree in software engineering from
Guangxi Normal University, Nanning, China, in 2017 and his M.S. degree incomputer application technology from Normal University, Nanning, China, in
2020. From 2020 to 2023, he was a teacher at Guangxi University of Chinese
Medicine. He is a CCF member and an ACM member. He has published morethan 10 papers in high-quality SCI journals. His research field includes
medical image processing, deep learning, and evolutionary computation.
(18775300556@163.com)

4652

Sensors and Materials, Vol. 35, No. 10 (2023)

Jiayan Yang is an undergraduate student in the Department of Medical
Information, Guangxi University of Traditional Chinese Medicine. She isengaged in the research of medical image segmentation with her supervisor
Daoqing Gong. (18178052551@163.com)

Xinyan Gan received her Bachelor of Engineering degree in Industrial
Management Engineering from Wuhan University of Technology, Wuhan,
China, in 1995. She pursued her Master of Engineering degree in Power
System and its Automation from Guangxi University, Nanning, China, from
September 2009 to May 2013. Her studies in this program were focused on
studying information management and information system design anddevelopment. (gan-yan_88@163.com)
Xiang Gao received his master's degree from Auburn University, U.S.A. He
now works in the School of Public Health and Management, Guangxi
University of Chinese Medicine. His main research area is artificial
intelligence. (gaox@gxtcmu.edu.cn)

Yuanxia Zhang received his bachelor's degree in computer software fromCentral China Normal University in 1998 and his master's degree incomputational mathematics from Dalian University of Technology in 2009.
His current interests include big data processing and intelligent algorithms.
(jgxyzyx@126.com)