Ultra-wideband (UWB) Based Classification Of Benign And Malignant Tumor

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1 Ultra-wideband (UWB) Based Classification Of Benign And Malignant Tumor 1 V. Vijayasarveswari, 1 S. Khatun, 1 M. Jusoh and 2 M. M. Fakir 1 Embedded Network and Advance Computing Research Cluster (ENAC), School of Computer and Communication of Engineering, Malaysia. 2 Institute of Engineering Mathematics, Universiti Malaysia Perlis, Perlis, Malaysia. Abstract Breast cancer is one of the main causes of womens deaths worldwide. Early detection of the breast cancer can reduce death risk. During the breast cancer detection, it is important to classify the type of the tumor whether it is benign or malignant for futher action. This paper presents the Ultrawideband (UWB) based classification of the tumor type based on dielectric properties (permittivity and conductivity). The developed system consists of a pair of home-made antennas as well as UWB transceivers to send and receive the signal. Two antennas are placed diagonally, at opposite sides of the phantom. UWB pulses are transmitted from one side of the phantom and received from the other side. K-fold based feed forward neural network is used to train, validate and test the collected signal. Four folds are used and each fold consists of 8 data samples. The detection accuracy of permittivity and conductivity are 88.57% and 92.67% respectively. Classification of tumor in the early stage will help user to receive treatment as soon as possible and can save precious human lives. Keywords: Breast Cancer, Benign, Malignant, Neural Network INTRODUCTION Breast cancer is a common disease among women. By definition, breast cancer is a group of abnormal cells developed in breast tissue [1]. Multiple symptoms are available to identify breast cancer, such as lumps on armpit, changes of the nipple and breast appearance, dimpling on the breast and rash on or around the nipple. Breast health checkup is required once the physical structure of the breast changed [2]. The tumor is usually grouped into two groups which are benign/non-invasive tumor and malignant/invasive tumor. A benign tumor is usually found in the duct of the breast and it is known as the early stage of breast cancer. Sometimes it neither grows nor endangers the life. In the other hand, a malignant tumor is cancerous. It can be found in the cells throughout the breast duct or occasionally in the lobule of the breast. The possibility of a malignant tumor to spread to different areas of the body is high [3] [4]. Thus, this type of tumor needs to be diagnosed soon. Traditional techniques that have been found and proved for clinical usages to detect breast cancer are mammogram, magnetic resonance imaging (MRI) scans, and ultrasound. Mammogram and MRI scans are widely used to identify the type of tumor [5]. However, the possibility of getting a false positive is high. At the same time, these types of techniques can be used if and only if some symptoms of breast cancer are identified because of the high cost and method of operation. Ultra-wideband (UWB) based imaging is proposed to overcome those shortcomings. UWB is a type of lowpower,high bandwidth and secure technology that uses radio energy to transmit the information. UWB is low-cost, secure and non-invasive. UWB technology is widely used and popular among researchers [6] [7]. Conceicao et.al., studied about classifying tumor by measuring the tumor s size and shape. Tumors are developed in different sizes and shapes. Shapes are developed based on Gaussian Random Spheres which are smooth, macrolobulated, microlobulated and speculated. The researcher has analyzed two different approaches, i.e. linear and quadratic which are used to classify the tumor. The quadratic approach is better in order to identify the shape and size rather than linear approach with above 90% accuracy. This research concludes that size and shape can be used to identify the type of tumor [8]. Lazebenik et.al., experimented on dielectric properties of normal, benign and malignant tumor using UWB. Tumor samples are taken from the hospital. Tumor samples are varied based on the types of surgeries undergone; lumpectomies, mastectomies, and excisional biopsies. Dielectric properties (conductivity and permittivity) are measured using a vector network analyzer (VNA). The research showed normal tissue s dielectric property value is large, malignant tumor s dielectric property value is high and benign tumor s dielectric property value is similar to normal tissue [9]. The above methods are only suitable for the clinic or hospital usage because of the high cost and manual operation of each system. Therefore, a low-cost breast cancer system based on UWB is proposed. In our study, the concept of Artificial Neural Network (ANN) is used to classify the types of tumor. ANN is the combination of program and data structure that imitate human brain decision making function. ANN consists of input data, hidden neuron, and hidden layers, as well as output. Feed forward Neural Network (NN) is widely used for classification due to its simplicity. For classification, the input node will directly go to the hidden layer and invoke the output node without any back loop [10]. MATERIALS AND METHOD Overall System Summary In this study, dielectric properties (permittivity and conductivity) are used to classify the type of breast tumor 8345

2 (benign and malignant). Permittivity is the ability of a material to store electrical energy while conductivity is the ability of a material to transmit electrical energy. Permittivity and conductivity values can be affected by frequency, temperature, orientation, mixture, pressure and molecular structure of the material.the proposed system consists of hardware and software. Hardware is included a pair of home made antenna and Ultra-wideband (UWB) transceiver. Software is included a neural network module. Figure 1 shows the flow chart of the methodology in brief. Figure 3: Experimental Set-up Figure 1: Overall Methodology Flow Chart Three signal samples are taken for each type of tumor. The captured signal are as shown in Figure 4 for benign type of tumor with and 2.53 for permittivity an1d conductivity respectively. The received signals are converted to dicrete from analogue.the total data samples generated are 42 data samples. Each data sample has 1632 data points that encode the permittivity and conductivity of the tumor. All received signal data are saved in a file in order to train, validate and test. Data Collection The experimental procedure to receive signal and data samples is based on [11]. In this experiment, the range of the frequency is 3.1GHz to 10GHz with centre frequency of 4.3GHz. Basically, the tumor is developed using the mixture of flour and water as shown in Figure 2. The amount of water varied for the different type of tumors. The size of the tumor is constant. Fourteen tumors with different dielectric properties are developed. Each tumor is placed in the developed breast phantom [12]. A pair of the home-made antenna is placed diagonally opposite on the breast phantom as shown in Figure 3. One antenna is used to transmit the signal while the other is used to receive the signal. a. Transmitted Signal b. Received Signal Figure 4: Captured Signal Figure 2: Tumor FEATURE REDUCTION Each data sample contains 1632 data points which is a large number of data points. This will increase the processing time and develop a complex structure. To overcome these shortcomings, feature reduction is done. Feature reduction is 8346

3 done to reduce the number of data points of each data sample. It will help to increase the system performance efficiency. Here, 1632 data points are reduced to 4 data points. 4 data points which are the mean, median, maximum value and minimum value. ARTIFICIAL NEURAL NETWORK K-fold based feed forward backpropagation neural network (NN) is used to train, validate and test the data samples. Backpropagation technique is to obtain better performance efficiency of the network. Basic feed forward backpropagation neural network is described as follows: net= newff (input, target, hidden neuron) This NN module is developed in Matlab software.the data samples are divided into 2 groups as follows: Group (1): First data set contains 32 data samples. This data set is for training, validating and testing of the proposed system. Group (2): Second data set contains 10 data samples. This data set is used to perform real-time testing. This testing will ensure the proposed system s efficiency. Data samples in first data set (Group 1) are divided into 4 subsets with each data set contains 8 data samples. The data sets are divided into training and testing as shown in Table 1.Table 2 shows the NN training parameters were used to train the data samples. Table 1: Training and Test K-Fold Training Testing Table 2: NN Parameters Used NN Parameter Values Output Number of nodes in Input layer 4 Permittivity Number of nodes in Hidden layer 1 15 and Number of nodes in Hidden layer 2 1 Conductivity Number of nodes in Output layer 1 Transfer function tansig Training function traingdm Learning rate Momentum constant 0.9 Maximum no. of Epochs Minimum performance gradient 1e-25 The training process is repeated until the training is optimized. NN consists of three layers which are input layer, hidden layer and output layer. Each layer consists of nodes which represents in circle. Lines show the flow of information from input layer to output layer. The amount of input node, hidden neuron and output node used are 4, 15, and 1 respectively as shown in Figure 5. 4 data points (after feature extraction) in each data sample are as input nodes are inserted into NN module. 15 hidden neurons in hidden layer 1 and 1 hidden neuron in hidden layer 2 are used. The performance of the NN module can be increased by changing the number of hidden neurons. However, the number of hidden neurons should not be very high because it will consume more time to complete the classification process. One output node is used to produce the output of the NN module. Figure 5: NN Architecture of Proposed System CLASSIFICATION OF BENIGN AND MALIGNANT TUMOR Benign and malignant is distinguish based on permittivity and conductivity values. Table 3 shows the appropriate permittivity and conductivity values to indicate the type of tumor [11]. The type of tumor can be determined if the permittivity value is less than 50 or more than 55. For values in between 50 to 55, further investigation is needed. Table 3: Permittivity and Conductivity Values to Classify the Type of Tumor [11] Permittivity Tumor Type Conductivity Less than 50 Benign Need Further Investigation - Greater than 55 Malignant 2 34 Basically permittivity is expressed as complex number: ε = ε jε (1) where ε is dielectric constant and ε is dielectric loss factor. Dielectric loss factor can be described in terms of conductivity and frequency: ε = σ ꙍ (2) where σ is conductivity andꙍ = 2πf c. So, the permittivity can be expressed in terms of conductivity and dielectric constant: ε = ε σ j 2πf c (3) where f c is centre frequency. Mean Square Error (MSE) MSE is the average squared error between output of network and target. After the training is done, the MSE of proposed 8347

4 NN module is calculated as define as follow: MSE = 1 j (t j j y j ) 2 (4) where j is number of input, t is actual target, and y is NN output. Real Time Testing The trained network is tested using data samples in Group (2). Then, the performance accuracy is calculated as below: Accuracy (%) =100 (( O A A ) 100) (5) where O is the NN s output and A is actual target. A Graphical User Interface (GUI) is developed as part of system in Matlab. It is an independent executive file where can be install at any device and use conveniently. Detected tumor can be visualized either in 2D or 3D as per user choices as shown in Figure 6 and Figure 7. This will help user to identify the seriousness of their breast health without anydifficulties at home and check with doctor for further investigation. RESULTS Table 5 shows the output of the real time testing for different dielectric properties of the tumor. Tested permittivity and conductivity are only until 38 and 4.0 S/m respectively. This is because the water-flour mixture becomes too sticky if more water is added [11]. So to form the tumor is very difficult. The average accuracy to detect the type of the tumor is 88.57% and 92.67% for permittivity and conductivity respectively. Table 6 shows the comparison of accuracy in classification of the type of tumor with different type of methods used in previous studies [8] [11]. The average accuracy of proposed system is 90.62%. It can be seen that the performance accuracy has been increased by using k-fold cross validation, which is around 9.8% improvement byshowing its superiority compared to proposed method in [8]. Figure 6: 2D Environment Table 5: Actual Targets, NN Outputs, and Detection Performance Accuracy Actual Target (mm) NN output (mm) Detection Performance Accuracy (%) Permittivity Conductivity Permittivity Conductivity Permittivity Conductivity Table 6: Comparison of Performance on Breast Tumor Classification Classification Method Performance Accuracy (%) Linear Discriminant Analysis [8] Quadratic Discriminant Analysis [8] Feed Forward Backpropagation Neural Network [11] K-fold based Feed Forward Backpropagation Neural Network Figure 7: 3D Environment CONCLUSION In this paper, UWB based non-invasive and low-cost early breast tumor type detection system has been proposed and demonstrated. This system will help home users and doctors in conducting regular check-ups to diagnose breast cancer and identify the type of the tumor efficiently anywhere and anytime. Appropriate action can be taken by a doctor if the type of tumor can be identified in the early stages of breast cancer. Our present focus is to examine the permittivity and conductivity values for malignant tumor. 8348

5 REFERENCE [1] Cancerorg, What is Breast Cancer, Retrieved from uide/breast-cancer-what-is-breast-cancer,accessed on 10 April [2] Breastcancer.org, What Is Breast Cancer?, Retrieved from c/what_ is_bc, Accessed on 10 April [3] Cancer.org, Finding Non-Cancerous Breast Conditions, Retrieved from /womenshealth/non-cancerousbreastconditions/noncancerous-breast-conditions-finding-benign-brcond,accessed on 10 April [4] When to Worry About Breast Lumps, Retrieved from Accessed on 10 April [5] Benefits And Limitations Of Screening Mammograms (BreastScreen NSW), Retrieved from Breastscreen.nsw.gov.au/aboutmammograms/benefits-and-limitations Accessed on 10 April [6] Kshetrimayum R. S., 2009, April, An introduction to UWB communication systems, IEEE Xplore, pp [7] Reza K. J., Khatun S., Jamlos M. F., Fakir M.M, Ikram-e-Khuda, &Ishwar Z., Proficient feature extraction strategy for performance enhancement of nn based early breast tumor detection, International Journal of Engineering and Technology, 5(6). [8] Conceiçãao, R. C., Medeiros M., O'Halloran, M., Herrera D. R., Tapia D.F. &Pistorius S., 2014, SVM-based classification of breast tumour phantoms using a UWB radar prototype system, IEEE. [9] Lazebenik M. &et.al., 2007, A large-scale study of the ultrawideband microwave dielectric properties of normal breast tissue obtained from reduction surgeries, Phys. Med. Biol, 52 pp [10] Rooh F., 2013,"Artificial Neural Network Approach to Clustering," The International Journal Of Engineering And Science (IJES). 2( 3), pp [11] AlShehri S. A, Khatun S, Jantan A. B, Raja Abdullah R. S. A, Mahmood R, Awang Z, 2011, 3D Experimental Detection And Discrimination Of Malignant And Benign Breast Tumor Using NN- Based UWB Imaging System, Progress In Electromagnetics Research. 116, p.p ,. [12] AlShehri S. A, Khatun S, Jantan A. B, Raja Abdullah R. S. A, Mahmood R, Awang Z, 2009 Experimental Breast Tumor Detection Using NN-Based UWB Imaging, Progress In Electromagnetics Research.111, p.p