**4. Results**

*Artificial Intelligence - Latest Advances, New Paradigms and Novel Applications*

complexity of processing. To overcome this complexity, we divide the image into many image blocks and process each part of the image independently. By using a programming parallel method like MapReduce, we can execute the processing of these blocks at the same time, thus saving processing time compared to traditional methods. **Figure 6** outlines how FCNN reconstructs CT low-dose images into Spark. In fact, the Spark architecture allows us to develop effective and appropriate techniques to utilize a large number of images. **Figure 6** outlines image processing in Spark. Training our FCNN model on the Spark framework involves two main steps (MapReduce programming), these steps will happen repeatedly and repeated

*FCNN-based spark map reduce pipeline for low-dose CT image reconstruction.*

until the total initialization error is small enough: Map and Reduce Step [4].

The scenario or concept of **Figure 6** allows us to process many CT images at the same time and optimize the processing time. Using the Spark framework and using the DL architecture, the process of dose optimization in pediatric skull scans is

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**Figure 6.**

**Figure 5.**

*Apache spark features.*

complete, easy and fast.

In this section, we are based on the architecture proposed in **Figure 6** and implement our CT image noise reduction algorithm. Our goal is to use FCNN to learn Eq. (1) by minimizing function of equations presented in [4]. We treat the image from Kaggle [46] as a clean/real image: . The data set contains information on 37 women and 45 men, so a total of 82 patients obtained 4615 CT images. However, due to insufficient computer capabilities, we reduced the number of images. **Figure 7** shows us some noisy and clean images from the dataset. For each pixel, we will generate a noisy version by adding Gaussian white noise: = + b (see Eq. (1)), where b where is a CT image, where each pixel is an independent implementation of zeromean Gaussian distribution, Has a standard deviation σ = 30.

Indeed, when we reduce the dose during the CT scan, the captured image is noisy. Here, we treat the noise as a Gaussian distribution. Since the sizes of CT images are different, we will consider random crops with a size of 180 × 180. As mentioned in [47], it is very important to initialize the weights in the process of training the model. The training loss and training PSNR according to number of epochs are also presented in this section. The PSNR is defined in [7, 48] by (2)

$$PSNR = 10\log\_{10}\left(\frac{25\,\text{S}^2}{MSE}\right) \tag{2}$$

$$\text{Where } MSE = \frac{1}{M \times N} \sum\_{i=0}^{M-1} \sum\_{j=0}^{N-1} \left( \varkappa(i, j) - \mathcal{y}(i, j) \right)^2 \tag{3}$$

**Figure 7.** *Clean and noisy CT images.*

PSNR gives an objective measure of distortion; a higher PSNR (greater than 30 dB) equals good image quality [7, 48]. **Figure 8a** and **b** respectively show the training loss and training PSNR according to several periods. We notice that in **Figure 8**, the training loss is close to 0.001, which proves the effectiveness of our training model, and the training PSNR is close to 33 dB (**Figure 8b**). Therefore, our DL method can efficiently denoise CT scan images. This effect can be seen

**Figure 8.** *(a & b) Results of training model. (a) Training Loss (b) Training PSNR.*

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*Big Data Framework Using Spark Architecture for Dose Optimization Based on Deep Learning…*

**Number of epochs Training Loss PSNR (dB) Training Time (s)** 0.021 22.6 423 0.014 26.7 850 0.011 29.8 1621 0.003 32.6 3112

in **Figure 9**, where we show a noisy and denoised image. To implement this work, we use a computer with Ubuntu OS, Spark and work locally in one cluster that we

Nowadays, new workflows, pipelines, and architectures are always suggested in other areas to improve the field of biomedical imaging. This work proposes a workflow for CT low-dose image reconstruction relying on FCNN and Spark. The uniqueness of our workflow is that it gives the best techniques, methods and algorithms that can be used in every design phase. By using the features of MapReduce, we can perform parallel processing on the proposed architecture. Based on the observations in the previous section, our proposed pipeline and architecture have a new concept for low-dose optimization in pediatric skull scans. They can be customized and adapted to many other biomedical applications. In order to effectively understand our proposed architecture, we compared this architecture with another architecture suggested in the literature. In [8], the author proposed an architecture based on FCNN for CT low-dose optimization. However, his proposed architecture is not based on the Spark, so it cannot process many bio medical images at the same time. As shown in [8], we propose two main training steps: forward propagation, in which low-quality images are passed through the network, and the output is obtained by calculating a set of convolutions. Backpropagation, where the derivative of the loss function with respect to each network parameter is calculated, and the calculated gradient is used to update these values to reduce the loss. Similarly, in [49], the author designed a DL architecture for CT reconstruction based on the plug-and-play framework, and obtained good results. Nevertheless, the authors did not use DL for low-dose reconstruction. They are only used for image noise reduction. As mentioned in Section 2, they did not rely on the literature of the Spark framework for CT low-

Deep learning has shown encouraging results in clinical studies because they can perform major reconstructions during a reduced-dose CT scan while maintaining a useful diagnosis. In this article, we outline some important research in the field of low-dose CT optimization, and study the problem of low-dose CT reconstruction from the perspective of DL. We propose a pipeline for low-dose image reconstruction using FCNN to Spark framework. To design our pipeline, we conducted a literature review to determine the most suitable method for CT low-dose image

built with one node. **Table 1** present a summary of our different results.

*DOI: http://dx.doi.org/10.5772/intechopen.97746*

*Summary of our Training simulation Model.*

**5. Discussion**

**Table 1.**

dose reconstruction using DL.

**6. Conclusion**

**Figure 9.** *Image noisy and obtained image denoising from our model.*

*Big Data Framework Using Spark Architecture for Dose Optimization Based on Deep Learning… DOI: http://dx.doi.org/10.5772/intechopen.97746*


**Table 1.**

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*(a & b) Results of training model. (a) Training Loss (b) Training PSNR.*

PSNR gives an objective measure of distortion; a higher PSNR (greater than 30 dB) equals good image quality [7, 48]. **Figure 8a** and **b** respectively show the training loss and training PSNR according to several periods. We notice that in **Figure 8**, the training loss is close to 0.001, which proves the effectiveness of our training model, and the training PSNR is close to 33 dB (**Figure 8b**). Therefore, our DL method can efficiently denoise CT scan images. This effect can be seen

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**Figure 9.**

**Figure 8.**

*Image noisy and obtained image denoising from our model.*

*Summary of our Training simulation Model.*

in **Figure 9**, where we show a noisy and denoised image. To implement this work, we use a computer with Ubuntu OS, Spark and work locally in one cluster that we built with one node. **Table 1** present a summary of our different results.
