# Classification of Adulterated Particle Images in Coconut Oil Using Deep Learning Approaches
## 2 Literature Review
### Convolutional Neural Networks  
The CNN method was introduced by [PERSON] which is a neural network with a convolutional layer added. It performs as a filter, defining the attributes of an input image and collecting various features from each image for classification. In 2019, [PERSON] [9] developed neocognitron. Unlike standard CNN, neocognitron was used to develop recognition of partly occluded patterns via the mechanism of selective attention. The backpropagation neural network was first stated by [PERSON] to help optimize prediction of data classification by internal restructuring using hidden layers. [PERSON] [10] tested backpropagation with a CNN on the Modified National Institute of Standards and Technology (MNIST) dataset, a handwritten numeric dataset. Then in 2019, they proposed deep learning hardware: past, present, and future which described the evolution of neural networks and deep learning affected by hardware and software improvements. In 2020, [PERSON] et al. [11] proposed improvements of backpropagation to be used for deep transfer learning. They used a pretrained model to transfer knowledge which learned from larger datasets to the target task. In 2007, [PERSON] et al. [12] first presented the use of backpropagation in collaboration with max-pooling CNNs (MPCNNs). Then, in 2018, [PERSON] et al. [13] proposed a CNN approach to skin cancer screenings. In 2020, [PERSON] and [PERSON] [14] presented a method for identifying the growth of melanoma cancer cells using a CNN approach. [PERSON] et al. [15] proposed ANN approach in particle automatic tracking to analyze particle localization and track particle motion from 2D and 3D collected video images obtained through a microscope. [PERSON] et al. [16] proposed a method for determining the turbidity level of water samples from the application of X-ray imaging systems. An ANN was then used to help determine the turbidity level. This proposed method was able to predict the change in turbidity level. It helped classify water quality levels and benefited industries that require high water quality. [PERSON] et al. [17] proposed two different convolutional neural networks using deep learning for automatically segmenting images and detecting different sizes of foreign objects that occur both naturally and man-made, such as dry leaves, scrap of papers, plastic scraps, and metal parts. This proposed structure was applied to walnut images. The experimental results had an accuracy of 99.5% in object segmentation and it was able to correctly classify 95% of the foreign objects and found that the segmentation and detection processing time of each image was less than 50 ms. [PERSON] et al. [18] proposed a method for weed detection from aerial photographs of soybean fields with drones. CNN was used for weed classification in four classes: soil, soybean, broadleaf, and grass weeds. They used the CaffeNet framework based on the AlexNet architecture model. The experimental results showed that CNN had an accuracy of 99% which was better than SVM, AdaBoost, and random forest. The core structure of a CNN consists of the three following principles: first is the input, which is used to receive information or object images as well as human vision. Second is the hidden layer, which is used to process the input data as well as the human brain and can learn to classify things. Last is the output, which shows the results obtained from the processing of the second part. The standard structure of a CNN is shown in Figure 1.  
Figure 1 describes the general structure of a CNN, which consists of the following parts:  
1. Input layer: reads the image input data prior to passing it to the neural network. 2. Convolutional layer: filters the image features from the analysis of each pixel of the image that has been read. The result is a convolutional feature map. 3. Rectified linear unit (ReLU): performs a nonlinear activation function. Figure 1: CNN architecture. * Pooling layer: provides a subsample rectified feature map to reduce linear dimensions and create a feature representation.