A New Idea in a Deep Learning Paradigm
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Biomed. Eng.-Biomed. Tech. 2018; aop Behrouz Alizadeh Savareh, Hassan Emami, Mohamadreza Hajiabadi*, Seyed Majid Azimi and Mahyar Ghafoori Wavelet-enhanced convolutional neural network: a new idea in a deep learning paradigm https://doi.org/10.1515/bmt-2017-0178 Keywords: brain tumor; convolutional neural network; Received October 13, 2017; accepted February 19, 2018 segmentation; wavelet transform. Abstract Purpose: Manual brain tumor segmentation is a chal- lenging task that requires the use of machine learning Introduction techniques. One of the machine learning techniques that has been given much attention is the convolutional Brain tumor is one of the main causes of death in all age neural network (CNN). The performance of the CNN can groups. According to reports by the National Brain Tumor be enhanced by combining other data analysis tools such Foundation (NBTF) and American Brain Tumor Associa- as wavelet transform. tions (ABTA), research in developed countries indicates Materials and methods: In this study, one of the famous that the number of people with this disorder has drasti- implementations of CNN, a fully convolutional network cally increased during the past decade [1–6]. (FCN), was used in brain tumor segmentation and its One of the routine tests in brain tumor diagnosis is architecture was enhanced by wavelet transform. In this the use of magnetic resonance (MR) imaging [7] which is combination, a wavelet transform was used as a com- capable of creating the optimal contrast of soft tissues plementary and enhancing tool for CNN in brain tumor [8]. Because of its high resolution and non-ionizing segmentation. nature, it is widely used in brain studies [9–14]. There Results: Comparing the performance of basic FCN archi- are many challenges in the manual segmentation of tecture against the wavelet-enhanced form revealed a brain tumors [12, 15–18], as one of the important steps in remarkable superiority of enhanced architecture in brain the processing of brain MR images [19], including differ- tumor segmentation tasks. ences in the size, shape, texture and intensity of tumors Conclusion: Using mathematical functions and enhanc- in the MR images. In fact, these challenges increase the ing tools such as wavelet transform and other math- manual segmentation error and lead to disagreement ematical functions can improve the performance of CNN among experts [20]. Besides that, the large number of in any image processing task such as segmentation and brain scans increases the time required for the analysis classification. of the MR images [21]. All of the mentioned problems turn brain tumor segmentation into a complex and time- consuming process and cause misdiagnosis or delay in decision-making [22, 23]. The presence of these prob- *Corresponding author: Mohamadreza Hajiabadi, Brain and Spinal lems illustrates the necessity of using machine learning Cord Injury Research Center, Neuroscience Institute, and Iranian techniques for automatic brain tumor segmentation [20, International Neuroscience Institute, Shariati Hospital, Tehran 24, 25]. University of Medical Sciences, Tehran, Iran, In recent years, the use of deep learning techniques E-mail: [email protected] has increased in the image processing as well as in the Behrouz Alizadeh Savareh: Student Research Committee, School of Allied Medical Sciences, Shahid Beheshti University of Medical medical image processing applications [26–29]. Among Sciences, Tehran, Iran techniques introduced for automatic brain tumor segmen- Hassan Emami: Faculty of Allied Medical Sciences, Shahid Beheshti tation [2, 26, 27, 30–39], the portion of deep learning-based University of Medical Sciences, Tehran, Iran techniques has rapidly increased [40] and several exam- Seyed Majid Azimi: Chair of Remote Sensing Technology, Technical ples of these techniques’ application have been recently University of Munich, Munich, Germany Mahyar Ghafoori: Department of Radiology, Hazrat Rasoul Akram proposed for brain tumor segmentation [30–33, 40–53]. Hospital, School of Medicine, Iran University of Medical Sciences, As a matter of fact, the best technique for exploiting the Tehran, Iran benefits of multidimensional spatial data such as image, Brought to you by | University of Sydney Authenticated Download Date | 5/30/18 9:08 AM 2 B. Alizadeh Savareh et al.: Wavelet-enhanced convolutional neural network sound and time series is a convolutional neural network Method (CNN) [34]. The CNN performance can be enhanced if it is combined with other data analysis tools. One of the most Idea description useful tools in signal and image analysis is the wavelet transform which is considered as a good candidate for the The deep learning hypothesis is based on the fact that, in order to CNN enhancement. In the following sections, the idea of achieve a high level of representation, a hierarchy of initial and mid- combining the CNN and wavelet transform is explained dle representations is required [71–74]. The CNN structure is based on the multilayered perceptron and then the application of this combination in brain (MLP) [75–77], and its function is similar to the time delayed neural tumor segmentation is investigated. networks which share the intra-network weights in order to reduce the computations [78]. Employing operations like convolution, sam- pling and linear correction unit, the CNN is able to directly extract features from raw data [79]. In fact, the use of the convolutional operation gives CNN spatial flexibility and the use of the sampling Related works results in higher levels of representation. The learning process occurs in the CNN using intermediate representations (known as the feature Evolution of brain tumor segmentation techniques map) [80], exactly the same as the hierarchical learning in biological brains [81]. The feature maps pass through layers, so the hierarchy of represents a move toward achieving an automatic and learning occurs and the information becomes more meaningful by accurate segmentation where three levels of algorithms going through the layers [82, 83]. This unique ability has led the CNN were developed to achieve these goals [1, 2, 10, 14, 36–39, to succeed in most of the image processing applications [35]. 54–58]. Along with the CNN properties in the hierarchical learning, a In the first generation, heuristic ideas were employed wavelet transform has interesting features that make it a candidate to enhance the CNN. The main functionality of the wavelet transform by algorithms such as the threshold level [59], area growth in image processing is the ability to decompose images into different [60] and edge detections [10]. Simplicity of the implemen- scales with different levels of details [84–86]. During the decomposi- tation was the main feature of these algorithms; however, tion, the separation of the information at different levels is repeated the big challenge was posed when they were faced with on the remainder part of the previous layer [79]. Therefore, it can be situations different from the training setting. deduced that CNN and wavelet transform have different approaches Techniques in the second generation were based toward image details in that CNN creates a high-level representation by upward combinations of low-level representations such as pixels, on the probabilistic and optimization methods such as lines and object elements, whereas wavelet transform decomposes artificial neural networks [61], Bayesian models [62], the image into its elements at different levels [75]. The difference fuzzy clustering [63] and support vector machines [64]. between the viewpoints of these two data analysis tools is the basis In addition, techniques like Gaussian mixture models, for an idea according to which wavelet transform can be considered linear and non-linear dynamic systems, conditional as an enhancing tool for the learning algorithm of CNN. In fact, according to the very idea, the compressed forms of the input image, random fields, maximum entropy (MaxEnt) models, as the result of various levels of wavelet decomposition, can be logistic regression, kernel regression and extreme learn- injected into the different layers of the CNN architecture and enhance ing machines were in this generation too. This group its performance. of techniques was effective in solving simple or well- constrained problems [65–68], but their low modeling capacity caused some problems while dealing with The application of this idea in brain tumor segmentation complex real-world situations [69]. There are techniques in the third generation which In this section, the application of the introduced idea in brain tumor seek to achieve the desired result using the higher levels segmentation is investigated and its details are described in the form of the implementation. of knowledge such as tacit knowledge, rules and models extracted directly or indirectly from data. Data: The Brain Tumor Segmentation (BRATS) dataset provided by The significant examples of this generation’s tech- the Medical Image Computing and Computer Assisted Intervention niques include Atlas-based segmentation [70] and Society was used in this study. There were 220 sample MR images of deep learning-based methods [40] which fascinatingly patients with a high-grade glioma, and 54 samples of patients with modeled the human brain information processing system. a low-grade glioma with 155 axial scans stored in three-dimensional Although various versions of deep learning techniques (3D) (mha) format for each patient. Each scan included