Safe Engineering Application for Brain Tumor Classification Using Deep Learning Advance Methods

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Journal of Green Engineering (JGE)

Volume-10, Issue-7, July 2020

Safe Engineering Application for Brain Tumor Classification Using Deep Learning

Advance Methods

1Ahmad Taha Abdulsadda and ²Suhad Al-Shoukry

1Associate Professor, Department of CTE, Al Najaf Technical Engineering College, Al Furat Al Awast Technical University, Al Najaf, Iraq.E-mail: [email protected]

2Assistant Lecturer, Al Najaf Technical Institute, Al Furat Al Awast Technical University, Al Najaf, Iraq.E-mail: ²[email protected], ²[email protected]

Abstract

In Brain cancer, is one of the main causes of cancer mortality for both males and females.The best way for the patient to improve. Early detection of potentially cancerous cells is a chance for survival. Notice also that none of the latest structures have been reached More than 98 per cent precision. In this paper we propose an optimal diagnostic system, not only to detect cancer early nodules and also to enhance the accuracy of Fog computing. The surrounding Fog is used to store the high volume CT scanned images to maximize privacy, low latency and support mobility. This paper evaluates core cuckoo concepts Algorithm.searching and its application to segmentation magnetic resonance imaging (MRI) brain tumour. The human brain is the most important structure where it is extremely challenging to identify the tumour as illnesses, because it is complex to differentiate the brain. components.Sometimes, the tumour can look as serious as regular tissues. The tumour, blood clot and other brain tissue areas look the same and complicate the work of the radiologist. Radiologist generally identifies the brain tumour through a thorough examination of MR pictures which requires a much longer period of time. The main inventive step is to construct a diagnostic method using the best method of optimization known as cuckoo

Journal of Green Engineering, Vol. 10_7, 3407–3420.

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3408 Ahmad Taha Abdulsadda et.al

hunting, which will allow the radiologist to obtain a second opinion on the existence or absence of a tumour.

Key words: Cancer, Classification, Optimization, Cuckoo search, Neural Networks.

1 Introduction 1.1 General Concept

Brain tumor is one of the key factors for the rise in child and mortality rates. A tumor is a mass of tissue that expands natural forces out of reach. A brain tumor develops as a cell group shifts and expands from its indexing and multiplies it in an irregular way.Unusual development of cells within the liver or inside the skin, which may be cancerous or non-cancerous. One type of cancer is tumour. Cancer starts with the cells, creating tissue-forming structures. Such materials do. Forme the body parts [1-4].

MRI devices are very critical for diagnostic imaging. Analysing.MRI is data of a multi-dimensional kind. Given in series from separate pulses.Could the MRI provide details of a specific disease that can classify a number of pathological conditions that provide a specific diagnosis [5-7]. Segmentation describes how the perpetrator is isolated project program MR picture area utilizing a number of design strategies. The segmentation process could be more efficient for the latest optimization algorithm used [8-10].

1.2 Literature Review

Because knowledge supply is overwhelming product lines, optimisation algorithms play a crucial role in the application of analytical knowledge.

Building a broad impact on a number of applications in engineering, enterprise, education and other fields.This research will demonstrate the prevalent cuckoo stalking, the temperament-enthusiast metaheuristic method used for optimization and computational awareness of brain tumour segmentation. Different methods of image segmentation are provided in bibliography. Azadeh et al have recorded another k-means clustering algorithm for background brain tissue segmentation, and pixels are isolated from Brain Inside standard brain pixels, [4].

Tsai et al. developed a System for histogram and biochemical segmentation Of tissues with MRI data[5]. AND Logeswari.Al.introduced a Map of Hierarchical Self-Organization (HSOM) for segmentation of tumours [3].And Ben George. Al. Bacteria Foraging Optimization program suggested innovative approach to separate tumour from brain images[2].

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Safe Engineering Application for Brain Tumor Classification Using Deep Learning Advance Methods 3409

The article is structured according to the following. Section 2 describes acquisition, pre-processing and development of brain MRIs product. section 3 aims at MR segmentation photos use algorithm Cuckoo search.

Section 4 executes evaluation of tests and efficiency of prescribed algorithm with the simulation setup and results. Section 5 presents this paper's conclusion.

2 MRI Collection, Pre-Processing and Exception Inclusive This section of the work is primarily aimed at having Brain MR files and data update by deleting the sound. The MR image is acquired in the first phase from McConnell Brain Imaging Generic Digital Databases Montreal Institute of Neurology Centre (MBIC) (MNI), Website of McGill University [7] and of the Harvard Medical School [10-15]. The ultimate cycle of brain tumour segmentation from in Figure 1 MR representations are clarified.

Figure 1: Brain tumour segmentation, adaptive from [6].

For this purpose of data processing a list of 200 T1 weighted photographs is chosen to be taken and used. As shown in figure 2 it is pre- processed in the second stage by utilizing the modified detection technique to remove all film artefacts.

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Figure 2: Image modified tracking algorithm, adaptive from [8].

In the third step, use the Hybrid Center Weighted Median Filter (HCWMF) to separate the high-frequency components from the MR image.

This HCWM filter is a combination of the Center Weighted Median Filter (CWMF) and the Wiener Filter (WF) which produces images of high quality for further processing[16-18] as shown in Figure 3.

Figure 3: CWME filter schematic diagram, adaptive [12].

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3 Segmentation Using Cuckoo Search

At Getting grey matter characterizes the average brain (GM), Cerebrospinal Fluid (CSF), White Matter (WM) and Fabrics. The anomalous brain normally includes the operative tumour, As for natural brain cells, necrosis and oedema. Necrosis is a dead cell within an active tumour whilst the Edema lies close to the active tumour boundaries. Edemas, which are mostly the product of local destruction of the blood brain barrier overlap of usual tissues and often impossible to deal of distinguish [18-22].An optimization problem requires the Finding. the right values. for a function.

The majority of the question of optimisation has a complexity in Finding accurate solution. Optimisation in a problem with MRF Consists of calculating the highest mutual likelihood over the picture, typically with some variables defined by some Data observed. That can be achieved by raising the overall energy content, [13-14], used to segment MRF hybrid with CS algorithm an image of the brain MR tumour. Initially a standard mark specific patterns are based on brain MR images.A 3×3 matrix kernel is randomly chosen from the improved image. The MRF is used. to measure a- posterior to full Value (MAP) for every kernel. Cuckoo Check (CS) implements three idealized laws:

 Every Bird of Cuckoo lays one egg at a time and place the egg in a randomly chosen nest;

 The nests with pre-eminent egg qualities the next generation will carry over.

 The amount of open host nests is fixed, and the probability for the host bird to locate a cuckoo egg is Pa ϵ[0 , 1], [22-25].

Here the setting parameters and all initialization value of the CS is done based particle swarm optimization as shown in figure 4.

Figure 4: Schematic Diagram PSO setting the CS parameters.

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The general outline of the cuckoo search algorithm is given in Figure 5.

This algorithm determines the optimum mark Gbest which will be used to locate the segmentation process threshold Locate the 3 x 3 block. with the optimum. mark and call the block's middle point, this value will be the segmentation process threshold. Place the pixels in a different picture has a greater meaning than the threshold, that is, the segmented image, [18-20].

The local random walk intended. for exploitation. of the search space. is implemented in init_nest function. and is mathematically expressed.,

(1) where and are. two different solutions selected randomly, H(u) is a Heaviside function, ε is a random number drawn from a uniform distribution, and s is the step. size. On the other hand, the global. random walk.

(implemented in generate_new_solution).

Intended for exploration of the search space is carried out by using Lévy flights expressed, as:

(2) Where

(3) the term L(s, λ) determines the characteristic scale and α > 0 denotes a scaling factor of the step size s. The characteristic scale L depends on the problem to be solved. In this case, modifications are smaller and therefore, prevailing the cuckoos to move too far in the search space, [20].

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Accordingly, trustworthy and automated classification scheme (shown in figure 6) is important to avoid human mortality rate. The automated diagnosis of brain tumours is a very difficult activity in broad spatial and structural heterogeneity of the local brain tumour area. Automatic brain tumour detection is proposed in this work, using the classification of Neural Networks (NN).

Figure 5: CS Brain tumour segmentation

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Figure 6: Brain tumour modelling.

The deeper design of the architecture is carried out using small kernels.

Neuron weight is given as low, [21-23]. The human brain is based on utilizing neural network architecture and implementation. The neural network is used predominantly for vector quantization, inference, data clustering, pattern matching, and function optimization and classification techniques.

After we got the enhancement image from the CS algorithm, then applying the NN based Classification Algorithm (shown in figure 7) as following, [24-28]:

1. Apply first layer Convolution Filter;

2. Smoothing the convolution filter (i.e) problem arising decreases filter intensity;

3. Activation layer manages the signal movement from one layer to another;

4. Usage of rectified linear unit (RELU) to correct the training cycle; in the subsequent layer the neurons are connected to each neuron

5. During training Loss layer is added at the end to give neural network feedback.

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Figure 7: CS and neural networks brain tumour classification system, adaptive [27].

4 Simulation Setup and Results

The effectiveness. of the proposed. methodology. is calculated. through parameters. such as responsiveness, specificity [20], Jaccard Similarity Index (JSI) [21], Dice Similarity Score (DSS)[22], and precision[24]. The following equations (1) to (5) was used to evaluate output parameters”. How to:

Sensitivity =Tp/(Tp+Fn) (1)

And, Specificity =Tn/(Tn+Fp) (2)

JSI =Tp/(Tn+Fn+Fp) (3)

DSS =Tp/(0.5*(Tn+2Tp+Fp)) (4)

Accurcy =Nr/N (5) If Tp is actually positive, Fp is false-positive, Tn is true-negative, Fn is false-negative, Nr is the number of correctly segmented MR brain images and N is the number of full MRI studies. These parameters are calculated for the CS segmentation algorithm, which produces an average sensitivity of 97.56 percent, which. implies that this algorithm. is capable of correctly segmenting tumour pictures. The algorithm's specificity is 95.43 per cent, which indicates the algorithm 's success to predict tumour absence.

The findings for segmentation using the CS algorithm are seen in Figure 5 below. Illustration 8(a) is an improved MR image of the brain as a guide to the Dividing algorithm. Figure 8(b) is the factual reality Segmented picture used to continue evaluating the split process.

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Figure 8: Original and CS segmentation simulation results.

The CS segmentation algorithm is based on 150 standard images to segment from it the tumor areas. The results are learned and tested using the NN with ground truth after segmentation, and the following performance.

parameters. are analyzed. Where the simulation setup shown in figure 9.

Figure 9: Multi layer perceptron neural networks

CS-based brain tumour performance compared with the other swarm segmentation algorithm based optimisation algorithms like Particle Swarm Optimization, (PSO) and the Adaptive Fuzzy Neural networks (AFNN) [24].

The comparative results are listed in Table I.

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Table I: Comparative simulation results.

Method Sensitivity

(%)

Specificity (%)

JSI (%) DSS (%) Accuracy

(%)

PSO 96.5 85.2 94.23 95.78 93.2

AFNN 97.2 86.3 95.45 96.3 94.1

NN-CS 98.23 90.45 96.3 97.3 96.3

5 Conclusion and Future Works

The swarm-based optimal solution named the cuckoo quest was described in this article, and its implementation to the identification of brain tumors was analyzed and contrasted with other current techniques. Hybrid Center Weighted Median Filter initially smoothened and enhanced the MR brain images. The hidden Markova Field is used in brain MRI segmentation to identify the image pixels and measure their posterior feature values.

Efficient automated brain tumor prediction is achieved in this research by using neural convolution network. Simulation is provided using the language python. The accuracy is measured and contrasted with all other forms in state of the arts. To find the efficiency, the training accuracy, validation accuracy and validation loss are calculated

The results of the diagnosis are often given as tumor or normal photos of the brain. CNN is one of the deep learning methods which includes feed forward layers’ sequence. Implementation also uses Simulink matlab version 2018a. The net image store is used for classification purposes. This is one of the ones that have been pre-trained. Therefore, the instruction is only carried out for the final sheet. CNN also derives raw pixel value with the meaning of the size, width and height function. Finally, to achieve high accuracy, the Gradient Decent-based Loss function is applied. The accuracy of the training, the validation accuracy and the loss of validation are calculated. The quality in preparation is 98.23 percent. Similarly, the precision of validation is high, and lack of validation very low.

References

[1]Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM,

“GLOBOCAN 2008 v2.0 : Cancer Incidence and Mortality Worldwide”, International Agency for Research on Cancer, Lyon, France, 2013.

[2]E. Ben George, M. Karnan, “MR Brain Image Segmentation using Bacteria Foraging Optimization Algorithm”, International Journal of Engineering and Technology (IJET), Vol. 4, No 5, pp. 295-301, 2012.

[3]T. Logeswari, M. Karnan, “An Improved Implementation of Brain Tumor Detection Using Segmentation Based on Hierarchical Self Organizing

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Map”, International Journal of Computer Theory and Engineering, Vol.

2, No. 4,pp.591-595, 2010.

[4]Azadeh yazdan-shahmorad, Hamid soltanianzadeh, Reza A. Zoroofi,

”MRSI– Brain tumor characterization using Wavelet and Wavelet packets Feature spaces and Artificial Neural Networks”, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2004.

[5]Tsai .C, Manjunath B.S, Jagadeesan, R., ”Automated Segmentation of brain MR Images”, Pergamon, Pattern Recognition, Vol. 28, no. 12,pp.

pp.1825-1837, 1995.

[6]Y. Zhang, L. Wu, S. Wang, “Magnetic Resonance Brain Image Classification by an Improved Artificial Bee Colony Algorithm”, Progress in Electromagnetics Research, Vol. 116, pp. 65- 79, 2011.

[7]http://www.bic.mni.mcgill.ca/brainweb, accessed on 2013.

[8]http://www.med.harvard.edu/AANLIB, accessed on 2013.

[9]E. Ben George, M. Karnan, “MRI Brain Image Enhancement Using Filtering Techniques”, International Journal of Computer Science &

Engineering Technology (IJCSET), Vol. 3 No. 9, pp. 399-403, 2012.

[10]K. M. Passino, “Biomimicry of bacterial foraging for distributed optimization and control”, IEEE Control Systems Magazine, vol.22,no.3, pp. 52–67, 2002.

[11]Angela Barr, GiovanniCarugno, Sandro Centro, Georges Charpak, Garth Cruickshank, MarieLenoble and Jacques Lewiner, “Imaging Brain Tumors Using a Multi-Wire Gamma Camera and Thallium-201”, IEEE Nuclear Science Symposium Conference Record (Cat. No.01CH37310), 2001.

[12]I. A. Basheer and M. Hajmeer, “Artificial neural networks:

fundamentals, computing, design, and application”, J. Microbiol.

Methods, vol. 43, no. 1, pp. 3–31, 2000.

[13]S. Knerr, L. Personnaz, and G. Dreyfus, “Handwritten digit recognition by neural networks with single-layer training”, IEEE Trans. Neural Netw., vol. 3, no. 6, pp. 962–968, 1992.

[14]D. M. Joshi, N. K. Rana, and V. M. Misra, “Classification of brain cancer using artificial neural network”,International Conference Electronic Computer Technology (ICECT), 2010.

[15]F. Y. partovi and Murrain anandrajan, “classifying inventory using artificial neural network approach,” Comput. Ind. Eng., vol. 41, no. 4, pp. 389–404, 2002.

[16]D. C. Park, M. A. El-Sharkawi, R. J. Marks, L. E. Atlas, and M. J.

Damborg, “Electric load forecasting using an artificial neural network,”

IEEE Trans. Power Syst., vol. 6, no. 2, pp. 442–449, 1991.

[17]P. K. Nanda, “MRF model learning and application to image restoration and segmentation,” Ph.D Dissertation, IIT Bombay, 1995.

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[18]Xin-She Yang, Suash Deb, “Cuckoo search: recent advances and applications”, Neural Computing and Applications, vol.24, pp.169–

174,2014.

[19]http://software.intel.com/en-us/articles/why-optimizationmatters submitted by John Sharp, 2011.

[20]Abdesslem Layeb,“A novel quantum inspired cuckoo search for Knapsack problems”, International Journal of BioInspired Computation,vol.3,no.5, 2011.

[21]Dhivya M, Sundarambal M, Anand LN, “Energy Efficient Computation of Data Fusion in Wireless Sensor Networks Using Cuckoo Based Particle Approach (CBPA)”, International Journal of communications Network and Sciences, Vol.4. No.4, pp, 249-255, 2011.

[22]Jiann-Horng Lin, “A Metaheuristic Optimization Algorithm for the Synchronization of Chaotic Mobile Robots, International Journal on Soft Computing”, Vol. 4,no. 2, pp. 37-47,2013.

[23]Srishti, “Technique Based on Cuckoo’s Search Algorithm for Exudates Detection in Diabetic Retinopathy”, Ophthalmology Research: An International Journal, vol.2, no.1,pp. 43-54, 2014.

[24]Steven S. Coughlin and Linda W. Pickle, “Sensitivity and specificity-like measures of the validity of a diagnostic test that are corrected for chance agreement”, Epidemiology, Vol. 3, No. 2, pp. 178-181, 1992.

[25]Paul Jaccard, “The Distribution of the Flora in the Alpine Zone”, The New Phytologist, Vol. 11, No. 2, pp. 37-50, 2007.

[26]L. R. Dice, “Measures of the Amount of Ecological Association between Species”, Ecology, Vol. 26, No. 3, pp. 297-302, 2005.

[27]E. Ben George, M. Karnan,”Feature Extraction and Classification of Brain Tumor using Bacteria Foraging Optimization Algorithm and Back Propagation Neural Networks”,European Journal of Scientific Research (EJSR), Vol. 88 No. 3, pp. 327 – 333,2012.

[28]Hui Zhang, Jason E. Fritts, Sally A. Goldman, “Image segmentation evaluation: A survey of unsupervised methods”, Computer Vision and Image Understanding, vol.110, no.2, pp. 260–280, 2008.

Biographies

Ahmad T. Abdulsadda: received the B. Sc. degree in electrical engineering from Tickrit University, Iraq in 1997, M. Sc. degree in electrical engineering from Baghdad University, Iraq in 2000, and the PHd in Electrical and Computer Engineering Department at Michigan State University, Michigan, USA. He was a member of the faculty at Baghdad University, Iraq, from

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2000 until 2006. He is a member of the faculty at Technical Najaf College, Al Furat Al Awast University, Iraq, since 2006. He is currently an assistant dean of Al Najaf College of Technical Engineering, Al Furat Al Awast Technical University. His research interests include. robotic fish, input control systems, methods of nonlinear.

Suhad Al-Shoukry: Received her Bachelor of Computer Science from Babylon University, Iraq in 1996. and her MSc. In Information Technology from UKM, Malaysia, 2015.She is currently working in Al Najaf Technical Institute, Al Furat Al Awast Technical University as an Assistant Lecturer.

Her research interesting is the biomedical deep learning.”””