# Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks
## 3 Our Proposed Methodology
### Pre-Processing
A CNN typically has three layers: a convolutional layer, a pooling layer, and a fully connected layer. The convolutional layer implements a dot product between two matrices, where one matrix is the set of learnable parameters otherwise known as a kernel, and the other matrix is the restricted part of the receptive field. During the forward pass, the kernel slides over the height and width of the image-producing the image representation of that sensory region. This produces a two-dimensional model of the image known as an activation map that gives the kernel's response at each scan's spatial position. The sliding size of the kernel is called a stride. The pooling layer replaces the network's output at specific locations by deriving a summary statistic of the nearby results. This diminishes the spatial size of the representation, which decreases the required amount of computation and weights. The pooling operation is processed on every slice of the model individually. Neurons in this layer have full connectivity with all neurons in the preceding and succeeding layer, as seen in regular FCNN. The FC layer helps to map the representation between the input and the output. Figure 5 depicts our proposed model's architecture, defining the number of convolutional layers, ReLu layers, max-pooling, and fully connected layers. Our architecture has three convolutional layers+ ReLu layers and three max-pooling layers with one fully connected layer and a softmax layer. The first layer, the convolutional layer, maps our lung image dataset with multi-dimensional filters, which give us the first layer output. This in

Figure 2:9 Selected nodule candidates from the features of the lung CT scans. termediate output is fed as an input to the next layer. There are many kinds of pooling layers: max pooling, average pooling, sum pooling, and so forth. We chose max-pooling as it gives the maximum value in all the patches from the previous layer. Rectified Linear Unit is deployed as an activation layer for our proposed NG-CNN, which solves the adverse value problems and avoids computation expense f(x) = max(x,0). For our lung nodule dataset, the output can have many classification stages, such as: (i) the patient has no nodule if the nodule size is less than 1.5 mm, (ii) the patient has a treatable nodule if the size of the nodule falls within 1.5 mm to 3 mm, and (iii) the patient has severe nodule if the nodule size is more significant than 3 mm. However, there are still many stages in nodules, as most of them are treatable. If the patient has a nodule of size more than 1.25 mm thickness in our proposed methodology, then the algorithm's output will be positive, stating immediate attention. The outcome will be negative if the nodule size is less than 1.25 mm, resulting in binary classifications. Finally, the softmax layer is applied to deal with the classification process, and the function is used to categorize the probability distribution. Hence, the values are activated from 0 to 1, and the category with the highest probability is considered output. The experimentation results are provided in Section 4 exhaustively. ## 4 Results & Discussion

### Dataset

We examined our deep model in experiments using an openly available LUNA16 dataset of volume 160 GB. According to the scan, the total number of patients is more than 1600, with variable slices per patient. The dataset contains low dose CT images in DICOM (Digital Imaging and Communications in Medicine) format for capturing the metadata of patients and other medical imagery. Medical professionals commonly use DICOM. The header file is included in the dataset with essential information, such as patient ID and the slice's thickness. The LUNA16 dataset has diverse image quality as it is taken from several hospitals and multiple machines. The labels for the training dataset were confirmed by pathology diagnosis. Four expert radiologists diagnosed every patient, and the ground truth label file is annotated.