# Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks
## 2 Related Works  
In this section, the background works related to our methodology are discussed briefly. The first part of the section focus on papers that improve the accuracy percentage in the CNN model. The second part of the section scrutinize performance comparison between Apache Spark and GPU. [PERSON] et al. [21] trained deep convolutional neural network for classification of ImageNet dataset, which consists of 1.2 million HD images. The deep network consists of five convolutional layers, three fully connected layers, and one softmax layer. GPU was employed to train the dataset, and the authors proposed a novel regularization method called \"dropout\" to avoid overfitting. [PERSON] et al. [22] implemented segmentation using deep CNN with experiments conducted in GPU Nvidia. The authors concentrated on the segmentation of the lung images to categorize similar lung image patches. Experimentation was done in a small set of lung images with few hundreds of scans for both testing and training. Related works were done in References [23; 24] from Cambridge University, where pixel-wise segmentation in lung scans was implemented. The deep model consists of an encoder and decoder layer and a pixel classification layer. In Reference [25], the authors constructed a new dataset by transforming the original images. The medical field lacks labeled training images. Changing the original dataset to create more data is convenient to minimize the error rate and uplift deep model accuracy. Experimentation was conducted with various datasets, such as malignant nodules, artificial geometric tumours, non-cancerous, and combined. The results show that the transformed dataset can better capture CT scans' features than the original dataset. [PERSON] et al. [26], introduced the single layer and deep convolutional networks based on a prediction system for remote sensing data analysis. They launched the supervised deep convolutional network, which can better operate on multi and hyperspectral imagery fields. They concluded that the proposed method could not produce the optimal outcome with a high dimensional dataset with less labeled information. The authors in Reference [27] concentrated on the dataset and separated the dataset based on the nodules' volume. They enlarged the large lung nodule dataset to train the model from 756 to more than 35,000. Random cropping also further expanded the dataset's count as the author believes a deep model requires a broad set of training data. [PERSON] et al., in Reference [28], compared various deep learning algorithms and evaluated them by applying the LUNA16 dataset and reducing the confusion matrix's false positives. Algorithms, such as candidate detection, ISICAD, subsolidCAD, largeCAD, ETROCAD, and M5L, are used for comparison. The results showed that convolutional networks and the best combination of algorithms give a promising prediction for medical image analysis with a minimum false positive percentage. Similarly, [PERSON]. [29; 30] use various deep models to compare and analyze the best suitable algorithm for detecting lung nodules. Two deep neural networks, such as convolutional and recurrent neural networks, are combined as nodular deep. The experiments are conducted with 1200 scans from the LIDC-IDRI dataset and are evaluated using metrics, such as sensitivity and specificity. [PERSON] et al. [31] use sphere template double matching technique to search the possible nodule-like shapes in the lung image data and later used the CNN algorithm for final classification. [PERSON] et al. [32] use unsupervised segmentation to provide only a point label and used CNN for binary classification. Our work focus on pre-processing the CT lung dataset to remove the Gaussian noise, later final classification is done by our proposed model NG-CNN. [PERSON] et al. [33] proposed a framework that combines a deep learning algorithm and Apache Spark to utilize Spark's in-memory computing power efficiently.