Computer-Aided Diagnosis for Lung Diseases based on Artificial Intelligence: A Review to Comparison of Two- Ways: BP Training and PSO Optimization

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1 Available Online at International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 4, Issue. 6, June 2015, pg RESEARCH ARTICLE ISSN X Computer-Aided Diagnosis for Lung Diseases based on Artificial Intelligence: A Review to Comparison of Two- Ways: BP Training and PSO Optimization Assistant Lecturer: Eman Saleem Ibrahem Harba University Of Baghdad / College Of Arts / Media Unit and Informatics emanharba_1212@yahoo.com Abstract: An intelligent computer-aided diagnosis system can be help doctors to diagnose and determine the type of disease from medical imaging like diagnosis disease from X-ray image of chest. This paper study some method of integration of neural network like backpropagation neural network and particle swarm optimizing (PSO) to recognition the X-Ray of chest for some lung disease cases (like Tuberculosis, TB. etc.) along with the normal case. The aim of this paper to investigated computer-aided diagnosis (CAD) schemes to determine the probability for the presence of TB or Tuberculosis in lung using artificial neural networks (ANN) that were trained by a Backpropagation (BP) algorithm or by a particle swarm optimization (PSO). The experiments show that CAD based on used backpropagation for training neural network is much effective than the optimization with PSO for recognition side which appeared that BP achieved a good result reached to 96.4% compared with % for PSO for 64x64 image input size. The efficiency and recognition testes for training method was performed and reported in this paper. Keywords: computer-aided diagnosis, X-ray chest diagnosis; Medical images; Recognition; Neural network. 2015, IJCSMC All Rights Reserved 1121

2 1. Introduction CAD are one of the main subjects in diagnostic radiology and medical imaging researches. In recent years, the concept of computer-aided diagnosis has been the subject of much research and not a little controversy. Recently CAD has beginning widely applied in the field of medical imaging for diagnosis and detection many different types of abnormalities by use of different imaging modalities. The early effort for used computerized analysis for medical images were made in the 1960s, while the systematic and serious investigation on CAD has begun in 1980s with a change of fundamental in the concept for utilization of the computer output, from automated computer diagnosis to computer-aided diagnosis [1]. Neural networks are well known for their good performance in classification and function approximation, and have been used with success in medical image processing over the past years, particularly in the case of pre-processing (e.g. construction and restoration), segmentation, and recognition. The Backpropagation poses most places in pattern recognition field. The other neural techniques, i.e. Hopfield, Adaptive resonance theory, radial basis function, Probabilistic, convolution, and fuzzy, have also found their position in medical image detection and recognition [2]. The backpropagation algorithm BP is one of the popular learning algorithms to train self-learning feed forward neural networks. The BP algorithm involves the gradual reduction of the error between model output and the target output. It develops the input to output, by minimizing a cost gained measured over a set of training. The backpropagation algorithm is applied in feed-forward artificial neural networks ANNs. The aim of the backpropagation algorithm is to reduce the error, until the ANN learns the training data [3]. Optimization has been applied on neural network to optimized pattern recognition. There are many types of optimization for ANN like Genetic Algorithms, Swarm Intelligence (SI) etc. SI is an intelligent paradigm used to optimized solving problems that took its inspiration from the biological examples by flocking, swarming, and herding phenomena in vertebrates; it is one of the scientific fields that are closely related to natural swarms existing in nature, such as ant colonies, bee colonies, and rivers. Particle Swarm Optimization (PSO) incorporates swarming behaviours observed in flocks of birds, schools 2015, IJCSMC All Rights Reserved 1122

3 of fish, or swarms of bees, and even human social behaviour, from which the idea is emerged. PSO is a population-based optimization tool, which could be implemented and applied easily to solve various function optimization problems, or the problems that can be transformed to function optimization problems. As an algorithm, the main strength of PSO is its fast convergence, which compares favorably with many global optimization algorithms like Genetic Algorithms (GA), Simulated Annealing (SA) and other global optimization algorithms; for that we used PSO as a comparison optimization method to compare its recognition result with the with recognition based on BP [4]. This paper organized as follows. Section 1 generate training data matrix, which is input and output nodes. true X-Ray images for three lung cases taken (normal, TB, and Tuberculosis) has been used as input data and 3 output labeling, pre-processing operation for X-Ray chest images has been applied on that images; It consist of three parts, first part image enhancement to removed noise and not useful texture (ribs, windpipe, dusts, etc.), and the second part is to segment each part of lungs. Part 3 resized image to desired neural input size that should be equally in dimension (i.e. 8x8, 32x32, 64x64, etc.). After preprocessing operation done it combined it in one data matrix, which represents the input layer units and labeling. Section2 prepared Feedforward neural network and initialized weight. Section3 training neural network by two methods: Backpropagation and PSO for different input sizes, then getting weight updated for each method to used it in recognition process. Section4 recognition process to recognized x-ray images, the recognition has been applied on trained and non-trained images by used weights gotten from section3. Finally, compared the recognition results for each methods and the percentage of correction in recognition process to verify what is the best method in training process for computeraided diagnosis can be using. 2. NN Training Data The images used for training ANN are taken from true X-ray chest images to two cases of lung disease (Tuberculosis TB and tumor) beside the normal case and be used for building program data matrix. 2015, IJCSMC All Rights Reserved 1123

4 Table 1: X-ray images for lung Cases and its numbers Type of Images Number Of Images Tuberculosis X-ray 172 TB X-ray 163 Normal X-ray X-ray Image Pre-Processing Pre-Processing are applied on X-Ray images to removed not useful texture like (windpipe, or ribs, etc.) and separated effective region i.e. lungs tissue; it consist of two parts: Image enhancement part that used the morphological filtering algorithm type Area Openings, and segmentation part that used Connected-component algorithm. 3.1 Image Enhancement for X-ray Images This method consist of multiple operations. Firstly, the x-ray image has been converted to gray-scale to remove colour, secondly it converted to binary image, finally applying filter type Area Openings to remove unwanted feathers, as shown in figure Segmentation Method After applying enhancement process, the Connected Components algorithm used to detect lungs region in X-ray images to separate it from whole image, as shown in figure 1. (a) (b) Figure 1: (a) Original X-Ray Image for chest with gray scale (b) After applying pre-processing operations 2015, IJCSMC All Rights Reserved 1124

5 Input Data Generation Algorithm Input: Output: Chest X-Ray Inputs arrays and Outputs Labeling Step 1: Start. Step 2: Request Images from folders for each case of lung (TB, tumors and normal). Step 3: Convert to gray scaled. Step 4: Convert to binary. Step 5: Given the threshold value that used in area opening filter. Step 6: Enhance X-ray images by used Area Opening algorithm to remove unuseful features and pass it to segmentation process. Step 7: Extract lungs area by used connected component algorithm to remove unwanted region. Step 8: Reshape Lungs and recombined it. Step 9: Saved preprocessed images in 3 folders that represent 3 lung cases Step 10: End. Figures 2 have shown the image process result with samples of three lung cases. (a) 2015, IJCSMC All Rights Reserved 1125

6 (b) 4. Training Neural Network Figure 2: Image processing result for sample of: a. Tuberculosis X-ray lung image b. Normal X-ray lung image c. TB X-ray lung image The neural network prepared and optimized by used two ways: BP algorithm and PSO algorithm. The model is multi-layer perceptrons (MLP) which consists of an input layer, hidden layer and an output layer. Inputs are X-ray images that pre-processed as specified previously. Output layers are three labels represent lung cases (TB, tumours and normal). Initially random images are used as initial weight, after that a transfer function applied on the weighted, which transfers the output to next layer, which is the hidden layer, then, the neurons of hidden layer and input value is compared to threshold value and its result compared to original one which were found. If the results do not match, backpropagation algorithm is applied by which weights of previous connections are 2015, IJCSMC All Rights Reserved 1126

7 adjusted. Particle swarm optimization applied to Integrate back propagation neural network for optimizing. 4.1 Preparing the Data Befor training process it need to generate data matrix that includes input layers and output labels. At first the preprocessed images are loaded and resized to required size, in this paper we generate 3 data matrix for three sizes (8x8, 16x16 and 64x64). The 8 8, and grid of pixels is unrolled respectively into a 64, 256 and 1024 dimensional vectors. Each of these training images becomes a single row in data matrix. This gives an A by B matrix where every row is a training image for an X-ray lung digit image.... (1) The second parameter need for training NN is to determine the best number of hidden neurons. According to a Jeff Heaton of Heaton research, the best number be used in NN training should be 2/3 the size of the input layer, plus the size of the output layer [5] Input Layers = I = A * B, Output layers = y Number of Neurons = (Input Layers + Output Layers) * 2/3 = (I + y) * 2/3 The Number of Neurons for 8 8 = (64+3) * 2/3 = 44~ 50 The Number of Neurons for = (256+3) * 2/3 = 172 ~ 170 The Number of Neurons for = (1024+3) * 2/3 = 684 ~ Implementation Feedforward neural network The first part of training is to implement Feedforward NN to optimize it by BP or by PSO. The sigmoid function is a mathematical function having an "S" shape (sigmoid curve) [6].... (2) 2015, IJCSMC All Rights Reserved 1127

8 Where: :, : can be a scalar, a vector, or a matrix When training neural networks, it is important to randomly initialize the parameters for symmetry breaking. One effective strategy for random initialization is to randomly select values for uniformly in the range. The learning rate epsilon ( has been used with value of 0.12, because this range of values ensures that the parameters are kept small and makes the learning more efficient. Having too many features, the learned hypothesis may fit the training set very well, the cost function J(θ), but fails to generalize to new testing data. To address this over fitting problem, one can reduce the number of features by manually selecting features to keep, but sometimes each of these features contributes a bit to predict the output and this method affects the classification accuracy. Regularization solves this problem by keeping all features but reducing magnitude values of parameter and by doing so, the cost function networks with regularization is given by: [7] for neural (3) Where: : the number of training examples, : total number of possible labels, : lambda, : the number of training examples, : number of hidden unit, : number of input units, : number of output labels, iscomputed for every example i, where: Sigmoid function, : theta one, : theta two. For the matrices Theta1 ( ) and Theta2 ( ) this corresponds to the first column of each matrix. This will add regularization to cost function. 4.3 Backpropagation Algorithm This part implements the backpropagation algorithm. The procedure for this BP algorithm can be summarized as follows: [8] 2015, IJCSMC All Rights Reserved 1128

9 Figure (3): Backpropagation Updates [8]. The gradient for the sigmoid function can be computed as... (4) Where: : For large values (both positive and negative) of z, the gradient should be close to 0. When z = 0, the gradient should be exactly Setting the input layer s values to the t-th training example. Perform a feed forward pass (Figure 8), computing the activations ( ) for layers 2 and 3. It needing to add +1 term to ensure that the vectors of activations for layers and also include the bias unit. Where: Unit in layer 1 (the input layer) Sigmoid ( ) Sigmoid ( ) 2015, IJCSMC All Rights Reserved 1129

10 For each output unit in layer 3 (the output layer), set Where: =... (5) {0, 1} indicates whether the current training Image matrix 0). belongs to class ( = 1), or if it belongs to a different class ( = 2. For the hidden layer = 2, =... (6) 3. Accumulating gradient from using the following formula: =... (7) After successful implemented of backpropagation algorithm, regularization is added to the gradient. To account for regularization, it turns out that this can be added as an additional term after computing the gradients using backpropagation. After has been computed regularization using this formula: using backpropagation, then it should to adding = = for... (8) = = for... (9) 4.4 Training Neural Network by PSO Swarm Optimization When the PSO algorithm is used in evolving weights of feed forward neural network, every particle represent a set of weights, there are three encoding strategy for every particle, the equation used for PSO optimization is shown in follows: [9]... (10) 2015, IJCSMC All Rights Reserved 1130

11 Where: c 1, c 2 are the acceleration constants with positive values; rand () is a random number between 0 and 1; w is the inertia weight. In addition to the parameters c 1, and c 2 parameters, the implementation of the original algorithm also requires placing a limit on the velocity (v max ). After adjusting the parameters w and v max, the PSO can achieve the best search ability. In particle swarm intelligence, a number of simple entities the particles are put in the search space of some function or problem, and each participle at current location estimate the objective function at this location. Each one of particle determines its movement over the search space through summation some aspect of its current history and best-fitness locations with those one or more members of the swarm. After all particles have been moved, the next iteration takes place. Finally, the swarm work as a whole, similar to a flock of birds that collectively foraging for food, is likely to move close to an optimum of the fitness function. Each individual in the particle swarm is composed of three D-dimensional vectors, where D is the dimensionality of the search space. These are the current position, the previous best position, and the velocity. Current position is evaluated as a problem solution. If that position is better than any that has been found so far, then the coordinates are stored in the second vector,. The value of the best function result so far is stored in a variable that can be called pbesti (for previous best ), for comparison on later iterations. The objective, of course, is to find the best positions and then updating and p besti. The new points are select by adding to, and the algorithm has been operate by adjusting, which can effectively be seen as a step size. 2015, IJCSMC All Rights Reserved 1131

12 The procedure for this PSO algorithm can be summarized as follows: 1. Step 1: Initialize a population array of particles with random positions and velocities on D-dimensions in the search space. 2. Step 2: loop. 3. Step 3: For each particle, evaluate the desired optimization fitness function in D- variables. 4. Step 4: Compare particle s fitness evaluation with its pbest i. If current value is better than pbest i,then set pbest i equal to the current value, and equal to the current location in D-dimensional space. 5. Step 5: Identify the particle in the neighborhood with the best success so far, and assign its index to the variable g. 6. Step 6: Change the velocity and position of the particle according to the equation (10) 7. Step 7: If a criterion is met (usually a sufficiently good fitness or a maximum number of iterations), exit loop. 8. Step 8: end loop. The parameter w, in the above PSO algorithm also reduces gradually as the iterative generation increases, just like the PSO algorithm. The flow chart of PSO program is shown in Figure (9) The iteration value used for trained ANN has been set to value of (1000) to get the best performance, the value of variable between -1 and 1, R are random number, the values of c1=1.5, c2 = 2.5 and the maximum velocity v max to be (0.1). 2015, IJCSMC All Rights Reserved 1132

13 Start Randomly initialize all particle position and velocities Next Particle Evaluate the fitness function for each particle Yes Stopping Criterion satisfied Modify P best If fitness of particle > P best No No Update velocity and position of particle Yes Modify G best If Max. No. of Iteration > P best No Satisfactory G best Yes End Figure 4: PSO Algorithm Scheme The tests was tested the efficiency of weights generated for different values of population size taken (100, 250 and 500). The weight efficiency, memory and computational time that have been achieved for each input have been classified in table (2). 2015, IJCSMC All Rights Reserved 1133

14 Table 2: Consumption of system resources and efficiency of weight generated for 1000 iteration, R1= (0.8), R2= (0.2), c1=1.5, c2=2.5, and v max = 0.1. Data Matrix Consumption of system resources Efficiency of Hidden Net Population Computational the weight Input layers layer CPU Requiremen size time / Sec generated units t of memory PSO % 90MB % PSO % 100 MB % PSO % 150 MB % PSO % 130 MB % PSO % 350 MB % PSO % 2400 MB % PSO % 3600 MB % PSO % 8400 MB % PSO % 17250MB % (a) (b) 2015, IJCSMC All Rights Reserved 1134

15 (c) (d) Figure 5: Cost gained with epochs of PSO with 100 population size for: a) 8x8 images matrix, b) 16x16images matrix, c) 32x32 images matrix, d) 64x64 images matrix. 5. X-ray Images Recognition Test (Practical Tests) Recognition has been tested using recognition program to identify lung case for each input image. First program has been tested on images that train used to train NN and then it been tested with images that are not trained. The program tested by used different weights that produced from two training method (backpropagation and the integrated way) for same iteration value of (1000), the backpropagation recognition results and PSO recognition results have been classified in table (4) and table (5). Table 4.6: Recognition Results for trained X-Ray images Image X-Ray lung image Cases Type of matrix Cancer (147) TB (96) Normal (119) Recognition size Detect Failed Detect Failed Detect Failed 1 BP 8x PSO 8x BP 16x PSO 16x BP 32x , IJCSMC All Rights Reserved 1135

16 64 x 32 x x 16 8x8 Eman Saleem Ibrahem Harba, International Journal of Computer Science & Mobile Computing, Vol.4 Iss.6, June- 2015, pg PSO 32x BP 64x PSO 64x Table 4.7: Recognition Results for non-trained X-Ray images Type of Recognition X-Ray lung image Cases Population Cancer (24) TB (15) Normal (22) size Detect Failed Detect Failed Detect Failed 1 PSO PSO PSO BP PSO PSO PSO BP PSO PSO PSO BP BP PSO Conclusion From the conducted experiments, we can get conclusions that for the following points: The emphasis of this paper is to develop a neural network training method used for building a program for X-Ray lung diagnosis that may help doctors in their diagnosis. 2015, IJCSMC All Rights Reserved 1136

17 The PSO-BP training results shown that the efficiency of weight updated increased depending on increasing of population size and number of iteration. The computational requirement especially memory have been increased rapidly as the input layer size increased and when the population size increased. The applying of image processing on X-ray images before trained it with neural network given advantage that reduce the error and increasing the efficiency that due to the removing un useful features that may be dispersal the NN and it made be possible to reduce the input layer (resized image to small size about 8x8) without fearing from data loses. PSO-BP recognition results of trained and non-trained images shown relatively reducing in recognition errors as the input size and population increased, for Tuberculosis images detection (that total number 147 images), the tested have been appeared an increased in correction detection from (122 images) for 64 input size to be raised to (133 images) for 4096 input size which improved about (7.47%), and four non-trained Tuberculosis images detection (that total number 24 images), the tested have been appeared an increased in correction detection from (16 images) for 64 input size to be raised to (19 images) for 4096 input size for same population size which improved about (12.5%). For the same input size of 64 with different population sizes, the tested have been appeared an increased in correction detection of TB images from (2 images) for 100 population size to be rise to (11 images) for 250 population size and to (14 images) for 500 population sized which improved about (80 %). References 1- Kunio Doi, Computer aided diagnosis in medical imaging: Historical review, current status and future potential, Computerized Medical Imaging and Graphics, Vol.31, No , (June 2007). 2- Zhenghao S., Lifeng H., Tsuyoshi N., Kenji S. and Hidenori I., Survey on Neural Networks Used for Medical Image Processing, International Journal of Computational Science, Vol.3, No.1, pp , ISSN: , (2009). 2015, IJCSMC All Rights Reserved 1137

18 3- Dike U. I., Adoghe U. A., Computer-aided diagnosis in medical imaging: Historical review, current status and future potential, International Journal of Computers and Distributed Systems, Vol. No.3, Issue 2, ISSN: , (Jun-July 2013). 4- Abraham A., Grosan C. and Ramos V., Swarm Intelligence in Data Mining, Springer- Verlag Berlin Heidelberg, ISBN: , (2006). 5- Jeff H. Introduction to Neural Networks with Java, First Edition, ISBN: X, (2005). 6- Balaji S. A. and Baskaran K., Design and Development of ANN System Using Sigmoid Activation Function to Predict Annual Rice Production in Tamilnadu, IJCSEIT International Journal of Computer Science, Engineering and Information Technology), Vol.3, No.1, February Ilunga M. and Stephenson D. Infilling stream flow data using FF-BP ANN Application of standard BP and pseudo Mac Laurin power series BP techniques, Water SA Vol. 31 No. 2, ISSN , (April 2005). 8- Ra ul Rojas, Neural Networks A Systematic Introduction, Book, Springer-Verlag, Berlin, Jing-Ru Zhang, Jun Zhang, Tat-Ming Lok and Michael R. Lyu, A Hybrid Particle Swarm Optimization Back-Propagation Algorithm for Feedforward Neural Network Training, Elsevier Inc., Applied Mathematics and Computation 185, , IJCSMC All Rights Reserved 1138

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