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Cervical cancer detection method using an improved cellular neural network (CNN) algorithm

Received Sep 25, 2018 Revised Nov 26, 2018 Accepted Dec 8, 2018 Cervical cancer is the second most common in Malaysia and the fourth frequent cancer among women in worldwide. Pap smear test is often ignored although it is actually useful, beneficial and essential as screening tool for cervical cancer. However, Pap smear images have low sensitivity as well as specificity. Therefore, it is difficult to determine whether the abnormal cells are cancerous or not. Recently, computer-based algorithms are widely used in cervical cancer screening. In this study, an improved cellular neural network (CNN) algorithm is proposed as the solution to detect the cancerous cells in real-time by undergoing the image processing of Pap smear images. A few templates are combined and modified to form an ideal CNN algorithm to detect the cancerous cells in total of 115 Pap smear images. A MATLAB based CNN is developed for an automated detection of cervix cancerous cells where the templates segmented the nucleus of the cells. From the simulation results, our proposed CNN algorithm can detect the cervix cancer cells automatically with more than 88% accuracy.


INTRODUCTION
Cervical cancer is a cancer of the cervix, which is commonly caused by a virus named Human Papillomavirus (HPV) [1]. The virus can damage cells in the cervix, namely, squamous cells and glandular cells that may develop into squamous cell carcinoma (cancer of the squamous cells) and adenocarcinoma (cancer of the glandular cells), respectively. Based on World Health Organization (WHO), cervical cancer is the fourth most frequent cancer in women after breast, colorectal, and lung cancers with an estimated 530,000 new cases in 2012 representing 7.9% of all female cancers [2]. Approximately 90% the 270,000 deaths from cervical cancer in 2015 occurred in low and middle-income countries [3]. Meanwhile, in Malaysia, the cervix cancer was the second common cancer among women, behind the breast cancer [4]. Rate of incidence of cervical cancer increased after reaching 30 years of age and peaks from 65 -69 years old. In Malaysian statistic, out of all the races, Indian women possess the highest occurrence subsequently behind is Chinese followed by Malay. 10.3 per 100,000 populations are the ASR for Indian females [5].
Pap smear screening test is the method used to look for pre-cancers, which are the cells that changed on the cervix that might become cervical cancer if they are not treated appropriately. The screening for cervical cancer will detect precursor lesions therefore allow early and potentially less invasive treatment that what is required for diseases that cause the symptoms. Pap smear test is a simple, effective and cheap, but it is not a disease specific test, which has low sensitivity and specificity [6]. Another disadvantage of Pap smear is that an abnormal Pap smear result does not always indicate cancer. Cells sometimes appear abnormal but

RESEARCH METHOD
The most important part in this project is designing the template and development of the algorithm to detect the cervix cancer by using Pap smear images. Prior to the development of the improved algorithm, the characteristics of each images need to be analyzed and CNN need to be understood fully. To design a template, the initial state, boundary condition, feedback and threshold value has to be studied beforehand.
A total of 13 images were collected from HUSM Kubang Kerian and another 102 images retrieved from Pap smear image database. A total of 115 images were used to simulate our developed MATLAB based CNN simulator. This MATLAB based CNN simulator is a user-friendly software. The output image will be shown at the end of the transients. This improved MATLAB CNN simulator is used for Pap smear images processing and to detect the cancerous cells. Flow chart of the algorithm for cervix cancer cell detection is shown in Figure 1.
The Graphical User Interface, which is in MATLAB, is utilized for the purpose of development of the CNN Simulator to detect the cervical cancer in the Pap smear image. These templates, which are designed for the detection of cervical cancer, are developed to reduce the consuming time to obtain the results. The process of using the MATLAB based CNN Simulator is easy by simply load the input image, run the image and the final simulated output image will be displayed at the end of the templates. The GUI of the improved Matlab CNN simulator is shown in Figure 2.

Template Design
Template 1 is a Blue Channel Extraction template that can be found in CNN library [27]. The template set contained of A (feedback), B (control) and Z (bias) template and they are shown as below: The template is applied in the Matlab CNN simulator and the result is shown as Figure 3.  From Figure 3, the simulated result cannot show the actual nucleus of the cell. To obtain the desired image, a modification on the template is made and is shown below. The modified template produced the result shown in Figure 4.
Template A = Template B =    Figure 4 shows that the modified template is able to filter out the nucleus of the cell. The unwanted background is filtered out and hence the output image is more presentable for the detection of abnormal cells. Next, Template 2 is used, which is the Contrast Enhancement template in order to proceed with the next step. The template is shown below and the simulated result is shown in Figure 5. The output of the image after applying the new modified Hollow Concave template is shown in Figure 8.  Figure 8 shows a clean image without unnecessary background and noises. The final output after applying the modified Hollow Concave template shows the small black nucleus that represents the abnormal cell.

RESULTS AND ANALYSIS
From this study, the information obtained from the simulation is the preliminary result, which can be used by the medical personnel for recommendation and final decision regarding further treatment of the patients, if an abnormal result is obtained. Medical personnel can decide whether the cells are cancerous or non-cancerous. The MATLAB based CNN can also be used by the medical practitioners or students as their guidelines in determining cancerous cells and as a learning purpose. Interpreting the images became easier and can reduce the consumed time.
The cancerous cells are invincible throughout the life until women finally decide on Pap Smear Test. To decide on an abnormal or normal cell, this automated cervix cancer cells detection which implementing the CNN algorithm can be used. The templates were modified and improved in order to be in sync with the Pap smear images given. The observation is based on the size of the nucleus and the number of multiplied cells in an image. As for this study, the nucleus is set as the parameters to be observed, and after the simulation, the white area represents the nucleus and black area represents the rest of the cell.
The simulation result in detecting cancerous cells in Pap smear image are shown in Figure 9 and Figure 10 for the example of abnormal cells, respectively. Figure 11 and Figure 12 show the simulation result of Pap smear image for the example of normal cells. It is observed that for abnormal cells, the black dot indicating the nucleus will appear at the final stage. Unlike for normal cells, the black dot (nucleus) will disappear after Template 3. The percentage for each type of abnormal cells is shown in Table 1 while Table 2 shows the percentage of accuracy for each type of the normal cells. From Table 1, it can be concluded that the overall accuracy for the 65 of abnormal images with 59 detected abnormalities is 90.77%. The accuracy is considered high for the detection of abnormal cervical cells. From Table 2, it is observed that 43 non-cancerous Pap smear images are correctly detected over total of 50 images and the accuracy is 85.54%. The limited number of noncancerous images is the main factor why the accuracy is quite low compared to the cancerous images.

CONCLUSION
CNN is frequently used in image processing, high-speed signal processing, video signal processing, biological visions and pattern recognition. CNN is implemented in this study particularly to detect the cervix cancer cells automatically by designing the templates and fully utilizing the function of MATLAB software. The templates are designed after considering essential parameters such as initial state, boundary condition, pixel value, control template, feedback template and threshold value. Based on the simulation results, 59 abnormal images were detected correctly, out of total 65 images. The obtained accuracy was 90.77%. For the non-cancerous images, 43 images were detected as normal cells, out of total 50 images with accuracy of 85.54%. For future improvement, more images need to be simulated under this MATLAB based CNN simulator for more accurate and precise results. Lack of images hinders the precision of the designed template. Every image has different composition, brightness and intensity. In order to fulfill and achieve the stability of the newly designed templates, more images needed for the simulation and analysis. In the future, classification can be made where the calculation of the size of the nucleus can be preceded for a high accuracy and precision.