# Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks
###### Abstract  
In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy. lung nodule; Apache Spark; Convolutional Neural Networks; deep learning +
Footnote †: journal: Medical  
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As aforementioned, erratic noise makes it tedious for the radiologist to distinguish between the nodules in the lung region [13]. For lower-dose examinations, the ability to determine relevant disease on noisier images will depend on various factors, including lesion intensity, lesion contrast associated with neighboring tissues, and image noise and sharpness. Applying data processing and image restoration approaches that minimize image noise while preserving spatial resolution makes it possible to increase the quality and diagnostic value of intrinsically noisy low-dose CT images. Therefore, noise removal plays an essential role as a pre-processing step in deep learning to enable the precise detection of lung nodules [14]. Deep learning is proven to be successful among researchers because of its ability to self-learn the feature values and provide an authentic outcome. NVIDIA's researchers performed trials on the ImageNet dataset to remove grains and noises without the need for observing the clean data [15]. The same technique can be applied to medical images where acquiring training data with ground truth is tedious. By proper model training, deep learning makes it achievable to enhance the image quality by removing noise and grains without training images. Deep learning can reconstruct the under-sampled CT images by training with the available data. It can extract more useful information from the unstructured data even without labeled knowledge by self-learning. This complexly integrated feature extraction and deep layers classification model easily overcomes the conventional machine learning algorithms [16]. Gaussian noise is an additive noise; removing it involves smoothing the distinct inside region of an image. These classical linear filters, such as the Gaussian filter, reduce noise efficiently but significantly blur the edges. A local measure of an image is to detect edges quantitatively and level them less than the rest of the picture. The contaminated pixels at the edges will be smoothed effectively using the multiple layers on the proposed Non-Gaussian Convolutional Neural Network (NG-CNN) architecture. Hence, all the regions of images are concentrated at numerous stages in terms of pixel by pixel. Thereby we attain qualified noise removed images. In our proposed Non-Gaussian Convolutional Neural Network (NG-CNN), we applied Gaussian noise removal as a foremost pre-processing step, which is later trained using the neural network's hidden layers. A significant part of noise removal is linear filtering and non-linear filtering. Non-Gaussian CNN's construction helps in embracing the progress in profound architecture, learning algorithms, and regularization methods into image denoising.