## 3 Methods and Materials
### Approach to Merging Features of CNN with Hand-Crafted  
In this section, a novel approach is applied to diagnose WSIs of abnormal cells of the cervical data set using ANN algorithm according to the fusion of features extracted using VGG-16 and GoogLeNet and their fusion with texture, color and shape features, which extracted by traditional algorithms (FCH, GLCM and LBP) [43]. The steps to implement this approach are: First, feed the enhanced cervical cancer data set to the VGG-16 and GoogLeNet models. Second, extract the features from the VGG-16 model and store it in vectors of feature so the data set becomes 25,000 \(\times\) 2048, and similarly extract the features from the GoogLeNet model and store it in the feature vectors so that the size of the features of the data set becomes 25,000 \(\times\) 2048. Third, features are high-dimensional; thus, the PCA was used for reducing the features of both CNN models so that the size of the data set features was 25,000 \(\times\) 512 for both models VGG-16 and GoogLeNet. Fourth, extract texture, color and shape features by FCH, GLCM and LBP algorithms and combine them into feature vectors. Texture, color and shape are the essential features for representing image data. The FCH algorithm is used to extract the color features. The color features are one of the most powerful features for diagnosing WSIs of LBC glass slide samples for cervical cancer [38]. Each local color has a histogram bin, and all colors of the histogram bin represent the colors of the region of interest (ROI). All colors in one histogram bin are the same (similar), and any colors in various histogram bins are various even if they are similar--the FCH method checks for color similarity by the membership value of each pixel. This algorithm extracts 16 color features from each image, and thus the method produces feature vectors of size 25,000 \(\times\) 16. The GLCM displays the ROI in a gray-level matrix. GLCM extracts texture features from ROI through the pixel values of a given region. A region is soft when it contains equal or close pixel values. In contrast, a region is coarse when it contains varying pixel values. Statistical metrics are extracted based on the spatial information of the GLCM to extract texture features. GLCM determines spatial information according to distance d and angle \(\theta\) between pixels [44]. There four angles to measure the relationship between pixels are  
Figure 5: The architecture of the ANN algorithm for classifying cervical cancer data set. \(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\) and \(135^{\circ}\) based on measure d. The GLCM extracts 13 texture features, so the method produces feature vectors with a size of 25,000 \(\times\) 13. The LBP describes the texture of surfaces by measuring local anisotropy. The image is converted to a gray-level array, and the target (central) pixel (\(g_{c}\)) is decomposed according to its neighboring pixels (\(g_{p}\)). In this work, the algorithm was set in size 5\({}^{\circ}\)5 each time a new central pixel was selected and analyzed according to 24 contiguous pixels as Equation (6). The method was repeated until all pixels were replaced with a new value according to the LBP algorithm [45]. This algorithm extracts 203 features from each image, so the method produces feature vectors with a size of 25,000 \(\times\) 203. \[LBP_{R,P}=\sum_{p=0}^{P-1}s(g_{p}-g_{c})2^{p} \tag{6}\]  
where \(g_{c}\) means the grey value of the center pixel, \(g_{p}\) means the grey value of contiguous pixels, \(R\) is the radius for contiguous, and \(P\) is all contiguous pixels. The features of the FCH, GLCM and LBP methods were fused into the same feature vectors so that the feature size of the data set became 25,000 \(\times\) 232. Finally, as shown in Figure 6, the features of the VGG-16 were fused with the hand-crafted features to become the size of 25,000 \(\times\) 744, then fed into ANN network for classifying.