# Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks
## 3 Our Proposed Methodology
### Pre-Processing
Spark has data locality,

Figure 9: A set of four subfigures: (**a**) describes the Performance comparison between Apache Spark, GPU, and CPU; (**b**) describes the Performance comparison between Apache Spark, GPU, and CPU by varying the batch size; (**c**) describes the Throughput of model workload; (**d**) describes the Throughput comparison between spark and GPU. better deal with failures and stragglers, and on top of everything, it is open-source. It's modern yet simple to use APIs to manage CPU, memory, and storage resources down to a granular level. Apache Spark is one of the most popular open source project in the Big Data landscape. Our model is trained with the available open-source dataset. The accuracy can be improved further when the model is deep with many convolutional layers. The dataset needs to be enriched by using data augmentation, which will increase the dataset's size in our future work. Thus, the model will not over-fit the data with deep layers and also gives maximum accuracy. Our future work involves data augmentation for dataset increment and training the model in a deeper network. Conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing--original draft preparation, writing, review and editing, N.J.V. ; supervision, D.R.S. ; project administration, C.S.N. ; funding acquisition, C.S.N. All authors have read and agreed to the published version of the manuscript. This research was supported by Basic Science Research Program through 631 the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF 632 2019R1I1A1A01063278). And this work was supported by INHA UNIVERSITY Research Grant. The dataset we used in our research is publicly available dataset and can be downloaded from the following website, [[https://luna16.grand-challenge.org/Data/](https://luna16.grand-challenge.org/Data/)]([https://luna16.grand-challenge.org/Data/](https://luna16.grand-challenge.org/Data/)). The authors declare no conflict of interest. ## References

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