# Artificial Intelligence Algorithm-Based Feature Extraction of Computed Tomography Images and Analysis of Benign and Malignant Pulmonary Nodules

Authors: Yuantong Gao; Yuyang Chen; Yuegui Jiang; Yongchou Li; Xia Zhang; Min Luo; Xiaoyang Wang; Yang Li
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

This study was aimed to explore the effect of CT image feature extraction of pulmonary nodules based on an artificial intelligence algorithm and the image performance of benign and malignant pulmonary nodules. In this study, the CT images of pulmonary nodules were collected as the research object, and the lung nodule feature extraction model based on expectation maximization (EM) was used to extract the image features. The Dice similarity coefficient, accuracy, benign and malignant nodule edges, internal signs, and adjacent structures were compared and analyzed to obtain the extraction effect of this feature extraction model and the image performance of benign and malignant pulmonary nodules. The results showed that the detection sensitivity of pulmonary nodules in this model was 0.955, and the pulmonary nodules and blood vessels were well preserved in the image. The probability of burr sign detection in the malignant group was 73.09% and that in the benign group was 8.41%. The difference was statistically significant (P<0.05). The probability of malignant component leaf sign (69.96%) was higher than that of a benign component leaf sign (0), and the difference was statistically significant (P<0.05). The probability of cavitation signs in the malignant group (59.19%) was higher than that in the benign group (3.74%), and the probability of blood vessel collection signs in the malignant group (74.89%) was higher than that in the benign group (11.21%), with statistical significance (P<0.05). The probability of the pleural traction sign in the malignant group was 17.49% higher than that in the benign group (4.67%), and the difference was statistically significant (P<0.05). In summary, the feature extraction effect of CT images based on the EM algorithm was ideal. Imaging findings, such as the burr sign, lobulation sign, vacuole sign, vascular bundle sign, and pleural traction sign, can be used as indicators to distinguish benign and malignant nodules.


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## Body

## 1. Introduction

In recent years, with changes in living environments and habits, cancer has become an important disease endangering human health. Approximately 5,161 people died of cancer last year, according to the Ministry of Health. In terms of the number of deaths caused by cancer, lung cancer ranks first in China [1]. At present, research on the causes and pathogenesis of lung cancer is probably related to smoking cigarettes, living environment, eating habits, patients’ own basic chronic diseases, and genetic factors [2, 3]. Lung cancer is a common malignant tumor mainly caused by bronchial mucosal lesions. Therefore, after the onset of lung cancer, patients mainly present with throat and lung discomfort, such as cough, chest pain, dysphonia, and other symptoms, which may also be accompanied by various complications, such as lung inflammation and malignant pleural effusion. The symptoms and precursors of lung cancer are related to its metastasis and progression [4]. The progression of lung cancer includes primary lung cancer, intrathoracic expansion of primary lung cancer, and metastasis to other sites of lung cancer [5]. The symptoms and precursors are mainly the following kinds. The first is primary lung cancer. Patients with primary lung cancer often have symptoms of cough and expectoration, which is characterized by irritant dry cough. The effect of cough suppressants is not obvious, and yellow purulent expectoration is associated with infection [6]. The second chapter is primary lung cancer with intrathoracic expansion. Patients may have hoarseness because the cancer compresses the recurrent laryngeal nerve. It may also be due to the metastasis of lung cancer cells to the mediastinum, the formation of mediastinal tumors, or mediastinal lymph node enlargement, resulting in compression of the esophagus and swallowing difficulties. The third chapter is lung cancer metastasis. Metastasis of lung cancer cells to corresponding tissues or organs will produce corresponding functional disorders, such as metastasis to the thoracic vertebra and the formation of bone tumors, which will cause pain in the thoracic vertebra [7, 8]. The progression of lung cancer in each period has the following precursor. First is a chronic cough. Patients often present with recurrent dry cough and a high metallic sound. The second is fever, with lung cancer fever at approximately 38°C. The third is hemoptysis, mostly with blood in sputum or intermittent blood sputum, occasionally with massive hemoptysis [9]. The fourth is dyspnea. Patients manifested chest tightness and asthma, and some patients exhibited chest pain. Fifth, lymphadenopathy may occur for unknown reasons. Unexplained emaciation may also occur [10].Pulmonary nodules are a common clinical sign, mostly round or irregular lesions with a diameter ≤3 cm in the lung, which can be single or multiple with clear or unclear boundaries [11]. The early stage of lung cancer is often characterized by pulmonary nodules, so the detection of pulmonary nodules plays a vital role in the diagnosis and treatment of lung cancer. At present, the examination methods of pulmonary nodules mainly rely on imaging methods, and different examination methods have their own advantages and disadvantages. In clinical practice, chest X-ray and computed tomography (CT) scans are usually the main examination methods, and CT examination is the main means for the detection and follow-up observation of pulmonary nodules [12]. Chest CT, as a noninvasive examination, is currently recognized as the most reliable and sensitive imaging examination method for pulmonary diseases. Compared with the X-ray film, the density resolution is significantly improved. In addition, CT images show the anatomical structure of the human cross section, which can provide more valuable information for the accurate localization of pulmonary nodules. Therefore, chest CT scans have an irreplaceable advantage over X-ray in the examination of lung diseases [13, 14]. Clinical evaluation and follow-up of pulmonary nodules mainly rely on CT scanning, especially high-resolution CT (HRCT), which can clearly show the location and morphological characteristics of nodules, so it is the preferred method for the detection and evaluation of pulmonary nodules at present [15]. Conventional CT scans can judge benign and malignant pulmonary nodules according to their morphology and density as well as the relationship between pulmonary nodules and adjacent tissues, with low specificity. Enhanced CT scans can reflect the blood supply of lesions to a certain extent, and the blood supply of malignant lung tumors is more abundant than that of most benign nodules [16]. In addition, with the rapid development of radiographic imaging technology in recent years, an increasing number of imaging techniques, such as enhanced scanning, energy spectrum technology, perfusion imaging, and magnetic resonance examination, have been applied in the detection, benign and malignant determination, and disease staging of pulmonary nodules. This provides more information for further detection and accurate diagnosis of pulmonary nodules [17].In this study, an artificial intelligence algorithm was applied to extract CT image features of pulmonary nodules, and the accuracy of the algorithm model extraction and classification was compared and verified. On this basis, the imaging performance of benign and malignant pulmonary nodules was observed to provide a reference for the clinical diagnosis of pulmonary nodules.


## 2. Materials and Methods

### 2.1. The Research Object

This was a retrospective experiment, and the CT image data of pulmonary nodules in the hospital from December 2019 to December 2021 were collected as the research object. There were a total of 298 patients with a total of 330 nodules, including 167 males and 131 females, ranging from 25 to 73 years old with an average age of 61.12 ± 10.46 years. There were 223 malignant nodules (malignant group) and 107 benign nodules (benign group). The experiment was approved by the ethics committee of the hospital.Inclusion criteria were as follows: (i) all patients were confirmed by thoracic surgery or clinical follow-up and had an accurate pathological diagnosis and definite clinical diagnosis results. (ii) The patient’s CT image was clear, and the focus was clear, which was conducive to labeling. (iii) The relevant clinical data of patients were complete.Exclusion criteria were as follows: (i) patients with pulmonary nodules who had not been pathologically confirmed or clinically diagnosed; (ii) incomplete patient data; (iii) the patient’s CT image parameters did not meet the requirements, and the definition was not enough.


### 2.2. The Research Methods

The CT imaging data of patients with pulmonary nodules were collected as the research objects. Based on the original lung nodule image, the EM algorithm [18] was used to extract the image features of lung nodules. The detection sensitivity and nodule feature extraction of the model were observed and recorded and compared with the existing pulmonary nodule classification model to judge its classification efficiency. On this basis, the imaging characteristics of patients with lung nodules were observed, the structural characteristics of benign and malignant lung nodules were compared and analyzed, and the imaging characteristics of benign and malignant lung nodules were summarized. The specific experimental process is shown in Figure 1.Figure 1
Technology roadmap.


### 2.3. Pulmonary Nodule Detection

The images containing pulmonary nodules were input into the flow and processed by the first five groups of convolutional neural networks of the ResNet50 network model, and the characteristic images of pulmonary nodules were obtained after processing. After the feature image, twelve anchor boxes generated by the RPN neural network sliding window were used for further processing to lock the area where the pulmonary nodules were located. The RPN neural network further eliminated the repeated regions in the selected regions, and the remaining regions after elimination were regions of interest (ROIs). The ROI containing location information was classified and regressed to obtain the probability value and location coordinates of pulmonary nodules [19].


### 2.4. Pulmonary Nodule Extraction

First, the lung parenchyma image was acquired, and the segmentation process was as follows. Binarization, domain filling, image fusion, and other operations were used for the basic lung CT images, and the lung binarization image of Figure2(a), chest binarization image of Figure 2(b), and chest image of Figure 2(c) were successively obtained. Figure 2(c) was subtracted from Figure 2(b) to obtain the binarization image of the lung parenchyma. Mask and fusion were performed to obtain the final image of the lung parenchyma. Next, the initial contour mask image of the lung nodule was generated according to the position coordinates of the lung nodule, and then, the initial contour image of the lung nodule was generated by fusion with the lung parenchyma image (Figure 2).Figure 2
Effect drawings of lung parenchyma image segmentation at each stage. (a) CT image of lung; (b) binarized lung CT image; (c) thoracic image; (d) chest cavity image; (e) binarized lung parenchyma image; (f) pulmonary parenchyma mask; (g) lung parenchyma image; (h) initial contour of lung nodules.
(a)(b)(c)(d)(e)(f)(g)(h)Next, the initial contour mask image of pulmonary nodules was generated according to the position coordinates of pulmonary nodules and then fused with the pulmonary parenchyma image to generate the initial contour map of pulmonary nodules. An expectation-maximization algorithm (EM) was used to segment the initial contour of pulmonary nodules. The steps were (1) initializing the distribution parameters; (2) according to the known distribution parameters, the expected value P of the optimal hidden variable was inferred through the training data set; (3) making maximum likelihood estimation on the basis of P and obtaining the distribution parameterM; and (4) circulating steps (2) and (3) until the expected value was reached to obtain the accurate contour map of pulmonary nodules [20]. The relevant equations of specific steps (2) and (3) are as follows.The measured data are a, the unmeasured data areb, and the sum of a and b is complete data, represented by y. M is a distribution parameter, the joint probability of complete data y is x (a, b|M), and the logarithmic likelihood function Z (a, M) of a can be expressed as(1)Za,M=logya|M=∫Za,b|Mdb.Then, the maximum likelihood estimation ofM is shown in the following equation:(2)Zmaxa,M=logya,b/M.When iteratedn + 1 times, the expectation T (M|M(n)) of the logarithmic likelihood function Z (a, M) of y is shown in the following equation:(3)TMMn=EZmaxM;y|a;Mn.When iteratedn + 1 times, M (n + 1) is expressed as(4)Mn+1=argMmaxTMMn.


### 2.5. Image Representation Analysis

The images of pulmonary nodules processed by the algorithm were observed. The marginal (spiculation sign and lobulation sign), internal (vacuole sign and bronchial inflation sign), and adjacent structures (vessel convergence sign and pleural traction sign) of benign and malignant nodules were compared.


### 2.6. Observation Indicators

The Dice similarity coefficient (DSC) and accuracy were calculated. Benign and malignant nodule margins (spiculation sign and lobulation sign), internal signs (vacuole sign and bronchial inflation sign), and adjacent structures (vessel convergence sign and pleural traction sign) were calculated. The DSC calculation is shown in (5), where m1 represents the image of lung nodules segmented by the gold standard, and m2 represents the image segmented by the algorithm.(5)DSC=2m1∩m2m1+m2×100%.


### 2.7. Statistical Methods

SPSS 20.0 was used for data analysis. Theχ2 test was used for disordered variables, and Spearman rank correlation was used for ordered variables. P<0.05 suggested that the difference was statistically significant.


## 2.1. The Research Object

This was a retrospective experiment, and the CT image data of pulmonary nodules in the hospital from December 2019 to December 2021 were collected as the research object. There were a total of 298 patients with a total of 330 nodules, including 167 males and 131 females, ranging from 25 to 73 years old with an average age of 61.12 ± 10.46 years. There were 223 malignant nodules (malignant group) and 107 benign nodules (benign group). The experiment was approved by the ethics committee of the hospital.Inclusion criteria were as follows: (i) all patients were confirmed by thoracic surgery or clinical follow-up and had an accurate pathological diagnosis and definite clinical diagnosis results. (ii) The patient’s CT image was clear, and the focus was clear, which was conducive to labeling. (iii) The relevant clinical data of patients were complete.Exclusion criteria were as follows: (i) patients with pulmonary nodules who had not been pathologically confirmed or clinically diagnosed; (ii) incomplete patient data; (iii) the patient’s CT image parameters did not meet the requirements, and the definition was not enough.


## 2.2. The Research Methods

The CT imaging data of patients with pulmonary nodules were collected as the research objects. Based on the original lung nodule image, the EM algorithm [18] was used to extract the image features of lung nodules. The detection sensitivity and nodule feature extraction of the model were observed and recorded and compared with the existing pulmonary nodule classification model to judge its classification efficiency. On this basis, the imaging characteristics of patients with lung nodules were observed, the structural characteristics of benign and malignant lung nodules were compared and analyzed, and the imaging characteristics of benign and malignant lung nodules were summarized. The specific experimental process is shown in Figure 1.Figure 1
Technology roadmap.


## 2.3. Pulmonary Nodule Detection

The images containing pulmonary nodules were input into the flow and processed by the first five groups of convolutional neural networks of the ResNet50 network model, and the characteristic images of pulmonary nodules were obtained after processing. After the feature image, twelve anchor boxes generated by the RPN neural network sliding window were used for further processing to lock the area where the pulmonary nodules were located. The RPN neural network further eliminated the repeated regions in the selected regions, and the remaining regions after elimination were regions of interest (ROIs). The ROI containing location information was classified and regressed to obtain the probability value and location coordinates of pulmonary nodules [19].


## 2.4. Pulmonary Nodule Extraction

First, the lung parenchyma image was acquired, and the segmentation process was as follows. Binarization, domain filling, image fusion, and other operations were used for the basic lung CT images, and the lung binarization image of Figure2(a), chest binarization image of Figure 2(b), and chest image of Figure 2(c) were successively obtained. Figure 2(c) was subtracted from Figure 2(b) to obtain the binarization image of the lung parenchyma. Mask and fusion were performed to obtain the final image of the lung parenchyma. Next, the initial contour mask image of the lung nodule was generated according to the position coordinates of the lung nodule, and then, the initial contour image of the lung nodule was generated by fusion with the lung parenchyma image (Figure 2).Figure 2
Effect drawings of lung parenchyma image segmentation at each stage. (a) CT image of lung; (b) binarized lung CT image; (c) thoracic image; (d) chest cavity image; (e) binarized lung parenchyma image; (f) pulmonary parenchyma mask; (g) lung parenchyma image; (h) initial contour of lung nodules.
(a)(b)(c)(d)(e)(f)(g)(h)Next, the initial contour mask image of pulmonary nodules was generated according to the position coordinates of pulmonary nodules and then fused with the pulmonary parenchyma image to generate the initial contour map of pulmonary nodules. An expectation-maximization algorithm (EM) was used to segment the initial contour of pulmonary nodules. The steps were (1) initializing the distribution parameters; (2) according to the known distribution parameters, the expected value P of the optimal hidden variable was inferred through the training data set; (3) making maximum likelihood estimation on the basis of P and obtaining the distribution parameterM; and (4) circulating steps (2) and (3) until the expected value was reached to obtain the accurate contour map of pulmonary nodules [20]. The relevant equations of specific steps (2) and (3) are as follows.The measured data are a, the unmeasured data areb, and the sum of a and b is complete data, represented by y. M is a distribution parameter, the joint probability of complete data y is x (a, b|M), and the logarithmic likelihood function Z (a, M) of a can be expressed as(1)Za,M=logya|M=∫Za,b|Mdb.Then, the maximum likelihood estimation ofM is shown in the following equation:(2)Zmaxa,M=logya,b/M.When iteratedn + 1 times, the expectation T (M|M(n)) of the logarithmic likelihood function Z (a, M) of y is shown in the following equation:(3)TMMn=EZmaxM;y|a;Mn.When iteratedn + 1 times, M (n + 1) is expressed as(4)Mn+1=argMmaxTMMn.


## 2.5. Image Representation Analysis

The images of pulmonary nodules processed by the algorithm were observed. The marginal (spiculation sign and lobulation sign), internal (vacuole sign and bronchial inflation sign), and adjacent structures (vessel convergence sign and pleural traction sign) of benign and malignant nodules were compared.


## 2.6. Observation Indicators

The Dice similarity coefficient (DSC) and accuracy were calculated. Benign and malignant nodule margins (spiculation sign and lobulation sign), internal signs (vacuole sign and bronchial inflation sign), and adjacent structures (vessel convergence sign and pleural traction sign) were calculated. The DSC calculation is shown in (5), where m1 represents the image of lung nodules segmented by the gold standard, and m2 represents the image segmented by the algorithm.(5)DSC=2m1∩m2m1+m2×100%.


## 2.7. Statistical Methods

SPSS 20.0 was used for data analysis. Theχ2 test was used for disordered variables, and Spearman rank correlation was used for ordered variables. P<0.05 suggested that the difference was statistically significant.


## 3. Results

### 3.1. Analysis of Pulmonary Nodule Detection Results

The total number of nodules was 330. In this study, the pulmonary nodule detection model based on the ES algorithm model detected 315 nodules, with a sensitivity of 0.955, which was higher than the existing three types of pulmonary nodule detection models (Table1). The free-response ROC (FROC) curve can measure the performance of the pulmonary nodule detection system, and the coordinates indicate the sensitivity of the system. The FROC curves and scores of the number of false positives in each case were compared. The FROC values of the pulmonary nodule detection model, ETROCAD model, M5LCAD model, and ZNET model in this study were 0.841, 0.665, 0.597, and 0.803, respectively. Therefore, the sensitivity of the model in this study was higher than that of the other three models (Figure 3).Table 1
Sensitivity of three existing pulmonary nodule detection models.
ModelSensitivityProposed yearETROCAD0.9292011M5LCAD0.7682015ZNET0.9152016Figure 3
Comparison of ROC curve and ROC curve scores of four types of pulmonary nodule detection models. (a) ROC curve of the four types of models; (b) corresponding ROC curve score. a: ETROCAD model; b: M5LCAD model; c: ZNET model; d: model based on ES algorithm.
(a)(b)


### 3.2. Analysis of Extraction Results of Pulmonary Nodules

By comparing the image segmentation effects of the two existing algorithms, it was found that the segmentation of the EM algorithm model considered the similarity between image pixel features and well-preserved pulmonary nodules and blood vessels (Figure4). In addition, from the DSC curves of the three segmentation methods, the EM algorithm model had the maximum DSC (Figure 5).Figure 4
Comparison of segmentation effects of different segmentation methods. (a) Original nodule image; (b) gold standard image; (c) LBF model image; (d) ACM model image; (e) EM model image.
(a)(b)(c)(d)(e)Figure 5
DSC curves of the three segmentation methods.


### 3.3. Comparison of Manifestations of Benign and Malignant Pulmonary Nodules

Figure6 shows the images of some benign and malignant nodules. The images of the two groups were compared, and it was found that the edges of benign pulmonary nodules were mostly smooth and had no surrounding burrs or fewer burrs, while the edges of malignant pulmonary nodules were uneven and mostly had burrs, and the overall shape of nodules was irregular.Figure 6
CT images of benign and malignant pulmonary nodules. The marked red areas in (a), (b), and (c) indicated benign nodules; the marked red areas in (d), (e), and (f) diagrams indicated malignant nodules.
(a)(b)(c)(d)(e)(f)The imaging findings of the benign and malignant groups were compared. In the malignant group, the probabilities of the spiculation sign, lobulation sign, vacuole sign, vessel convergence sign, and pleural traction sign were 73.09%, 69.96%, 59.19%, 74.89%, and 17.49%, respectively. The probabilities of the spiculation sign (8.41%), lobulation sign (0), vacuole sign (3.74%), vessel convergence sign (11.21%), and pleural traction sign (4.67%) were higher than those of the benign group. The difference was statistically significant,P<0.05. However, there was no statistically significant difference in the bronchial inflation sign between the two groups (P>0.05). The details are shown in Table 2 and Figure 7.Table 2
Comparison of benign and malignant pulmonary nodules.
ManifestationsBenign group (107)Malignant group (223)χ2PExistProportion (%)ExistProportion (%)Spiculation sign98.4116373.0917.325P<0.001Lobulation sign0015569.9636.484P<0.001Vacuole sign43.7413259.1919.502P<0.001Air bronchus sign1411.213113.900.1240.089Vessel convergence sign54.6716774.8939.790P<0.001Pleural traction sign003917.494.6390.041Figure 7
Proportion of benign and malignant pulmonary nodules. A, B, C, D, E, and F represent the burr sign, lobulation sign, vacuole sign, bronchial inflation sign, vascular set number sign, and pleural traction sign, respectively.∗ Compared with benign group, P<0.05.


## 3.1. Analysis of Pulmonary Nodule Detection Results

The total number of nodules was 330. In this study, the pulmonary nodule detection model based on the ES algorithm model detected 315 nodules, with a sensitivity of 0.955, which was higher than the existing three types of pulmonary nodule detection models (Table1). The free-response ROC (FROC) curve can measure the performance of the pulmonary nodule detection system, and the coordinates indicate the sensitivity of the system. The FROC curves and scores of the number of false positives in each case were compared. The FROC values of the pulmonary nodule detection model, ETROCAD model, M5LCAD model, and ZNET model in this study were 0.841, 0.665, 0.597, and 0.803, respectively. Therefore, the sensitivity of the model in this study was higher than that of the other three models (Figure 3).Table 1
Sensitivity of three existing pulmonary nodule detection models.
ModelSensitivityProposed yearETROCAD0.9292011M5LCAD0.7682015ZNET0.9152016Figure 3
Comparison of ROC curve and ROC curve scores of four types of pulmonary nodule detection models. (a) ROC curve of the four types of models; (b) corresponding ROC curve score. a: ETROCAD model; b: M5LCAD model; c: ZNET model; d: model based on ES algorithm.
(a)(b)


## 3.2. Analysis of Extraction Results of Pulmonary Nodules

By comparing the image segmentation effects of the two existing algorithms, it was found that the segmentation of the EM algorithm model considered the similarity between image pixel features and well-preserved pulmonary nodules and blood vessels (Figure4). In addition, from the DSC curves of the three segmentation methods, the EM algorithm model had the maximum DSC (Figure 5).Figure 4
Comparison of segmentation effects of different segmentation methods. (a) Original nodule image; (b) gold standard image; (c) LBF model image; (d) ACM model image; (e) EM model image.
(a)(b)(c)(d)(e)Figure 5
DSC curves of the three segmentation methods.


## 3.3. Comparison of Manifestations of Benign and Malignant Pulmonary Nodules

Figure6 shows the images of some benign and malignant nodules. The images of the two groups were compared, and it was found that the edges of benign pulmonary nodules were mostly smooth and had no surrounding burrs or fewer burrs, while the edges of malignant pulmonary nodules were uneven and mostly had burrs, and the overall shape of nodules was irregular.Figure 6
CT images of benign and malignant pulmonary nodules. The marked red areas in (a), (b), and (c) indicated benign nodules; the marked red areas in (d), (e), and (f) diagrams indicated malignant nodules.
(a)(b)(c)(d)(e)(f)The imaging findings of the benign and malignant groups were compared. In the malignant group, the probabilities of the spiculation sign, lobulation sign, vacuole sign, vessel convergence sign, and pleural traction sign were 73.09%, 69.96%, 59.19%, 74.89%, and 17.49%, respectively. The probabilities of the spiculation sign (8.41%), lobulation sign (0), vacuole sign (3.74%), vessel convergence sign (11.21%), and pleural traction sign (4.67%) were higher than those of the benign group. The difference was statistically significant,P<0.05. However, there was no statistically significant difference in the bronchial inflation sign between the two groups (P>0.05). The details are shown in Table 2 and Figure 7.Table 2
Comparison of benign and malignant pulmonary nodules.
ManifestationsBenign group (107)Malignant group (223)χ2PExistProportion (%)ExistProportion (%)Spiculation sign98.4116373.0917.325P<0.001Lobulation sign0015569.9636.484P<0.001Vacuole sign43.7413259.1919.502P<0.001Air bronchus sign1411.213113.900.1240.089Vessel convergence sign54.6716774.8939.790P<0.001Pleural traction sign003917.494.6390.041Figure 7
Proportion of benign and malignant pulmonary nodules. A, B, C, D, E, and F represent the burr sign, lobulation sign, vacuole sign, bronchial inflation sign, vascular set number sign, and pleural traction sign, respectively.∗ Compared with benign group, P<0.05.


## 4. Discussion

Lung cancer is a relatively common respiratory malignant tumor disease, and the most common clinical manifestations of this disease include expectoration, hemoptysis, fever, chest distress, and chest pain. However, as the above symptoms do not have classical specificity and most patients usually have no obvious clinical symptoms in the early stage, it is difficult to detect them in the early stage [21]. Lung cancer is classified into blame small cell lung cancer and small cell lung cancer and is classified into inchoate lung cancer, metaphase lung cancer, and terminal lung cancer according to local severity cent. Lung cancer is classified into four stages and nine small stages according to tumor size, lymph node metastasis, and distant metastasis [22]. Currently, the diagnostic methods for lung cancer include sputum cytology, thoracotomy exploration, X-ray examination, CT examination, and mediastinoscopy, which can distinguish pulmonary tuberculosis, pulmonary infection, and benign tumors [23]. Pulmonary nodules refer to pulmonary nodular lesions found in CT image examination. According to the size, shape, progression rate, and imaging characteristics of pulmonary nodules, the benign and malignant properties and etiology of pulmonary nodules can be predicted. Pulmonary nodules less than 10 mm have only a 1% possibility of malignancy, while the malignant degree of pulmonary nodules reaching 20 mm is greatly increased [24]. If pulmonary nodules are detected, attention should be given to the nature of the nodules, and it is necessary to actively look for the cause and perform a differential diagnosis. Regular review should be implemented for pulmonary nodules requiring continuous observation. Pulmonary nodules may be benign, such as pulmonary hamartoma, pulmonary infection, and pulmonary tuberculosis, or malignant, such as lung cancer and lung metastasis, depending on the external appearance and some characteristic values of the nodules. The early stage of lung cancer is often characterized by pulmonary nodules, so the detection of pulmonary nodules plays a crucial role in the diagnosis and treatment of lung cancer [25].Computerized tomography (CT) is a disease detection instrument with relatively complete functions. It can measure the human body according to the difference inX-ray absorption and transmittance of different tissues of the human body and then use highly sensitive instruments to measure the human body. After the data are processed by the electronic computer, the cross section or three-dimensional image of the inspected part can be obtained [26]. CT plays an important role in assisting doctors in diagnosing diseases. In recent years, with the progress of the computer level, an increasing number of artificial intelligence algorithms have been developed, and they are applied in the medical field to help doctors diagnose diseases accurately and efficiently. Among them, the ES algorithm proposed by Dempster, Laird, and Rubin is an unsupervised learning algorithm. This algorithm finds the potential information in the data by iteration, which is conducive to solving the optimization problem of hidden variables. By iteration, it finds the maximum likelihood estimation of parameters from the probability model and then obtains the information of hidden variables [27]. Therefore, in this study, the ES algorithm was used to extract the features of pulmonary nodule images, and its extraction effect was tested. The test results showed that the detection sensitivity of the ETROCAD model proposed by scholars in 2011 was 0.929 and that of the M5LCAD model proposed in 2015 was 0.768. The detection sensitivity of the ZNET model proposed in 2016 was 0.915, and the calculated sensitivity of the detection model based on the ES algorithm was 0.955, which was higher than those of the above three detection models. The higher the sensitivity is, the smaller the number of missed pulmonary nodules. Therefore, the model based on the ES algorithm in this study showed certain advantages in the initial detection stage of pulmonary nodules. In the aspect of lung nodule image segmentation, the image segmentation effects of the LBF model and dACM model were compared. The LBF model algorithm was not very effective in segmenting pulmonary nodules with vascular adhesion, while the ACM model algorithm had some errors in separating the edges of ground-glass nodules. The main reason was that these two methods were more concerned with the distance between pixels, while the information of pixels themselves was less. The segmentation of the EM algorithm model in this study considered the similarity between image pixel features and preserved lung nodules and blood vessels well. The DSC value can reflect the difference between the algorithm segmentation and the gold standard segmentation. The higher the DSC value is, the higher the overlap between the image segmented by the algorithm and the image segmented by the gold standard [28]. Compared with the LBF model and ACM model, the EM algorithm model had the highest DSC, which indicates that the image segmentation effect was the best. In addition, based on the extraction of the above imaging features of pulmonary nodules, a comparative analysis of the imaging manifestations of benign and malignant pulmonary nodules was performed. The comparative results showed that the probability of burr sign and lobulation sign in the malignant group was 73.09% and 69.96%, respectively. However, in the benign group, the probability of the burr sign was 8.41%, and the probability of the lobulation sign was 0. The difference was statistically significant (P<0.05). Compared with benign pulmonary nodules, the images of malignant pulmonary nodules often showed uneven edges and burrs. Among them, the burr sign refers to radial lines with different lengths and thicknesses distributed around the nodules. However, the burr sign is not unique to malignant nodules; it can also be seen in some benign nodules. Foliage refers to the uneven outline of nodules, showing a concave-convex shape composed of continuous arcs. The results showed that lobulation was unique to malignant nodules, but the research results of other related scholars showed that lobulation can also appear in a few benign nodules. The reason for this difference may be the limited experimental observation samples. The cavitation sign refers to a small (<5 mm) and irregular transparent area in the nodule. In this study, the detection probability of the cavitation sign in the malignant nodule group was 59.19% and that in the benign nodule group was 3.74%, which was significantly higher than that in the benign nodule group. Therefore, cavitation signs can be used as one of the diagnostic manifestations of lung cancer. The vascular bundle sign refers to the small blood vessels around the nodules that expand and are pulled to gather at the lesion and interrupt or pass through it at the lesion. The results showed that the probability of the vascular bunching sign in the malignant group was 74.89%, which was much higher than that in the benign group (4.67%), indicating that there were small blood vessels around most malignant nodules, which can be used as one of the manifestations to distinguish between benign and malignant nodules. Pleural traction refers to the linear or curtain shadow between the nodule and the adjacent pleura. The probability of pleural traction was 0 in the benign group and 17.49% in the malignant group, but this result was inconsistent with the existing research, which shows that some benign nodules also had pleural traction.


## 5. Conclusion

The imaging features of benign pulmonary nodules are usually smooth edges with no burrs or fewer burrs around them, while those of malignant pulmonary nodules are usually uneven edges with burrs, and there are small (<5 mm) and irregular bright areas inside the nodules, small blood vessels around the nodules or linear or curtain shadows between the nodules, and the adjacent pleura. The feature extraction and classification of pulmonary nodule images based on the ES algorithm had high sensitivity, and the image segmentation effect was good. Pulmonary nodule images based on the ES algorithm can be widely applied to the processing of clinical pulmonary nodule images, thus reducing the workload of doctors and improving the accuracy of diagnosis.



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*Source: 5762623-2022-09-14.xml*