## 4 Proposed Deep Learning Model for Malaria Detection  
In this section, we present a novel neural architecture for malaria detection from microscopic thin blood smear images. The proposed method can be divided into three parts, data preprocessing, feature extraction, and classification. Figure 2 shows these steps in a schematic diagram. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Model** & **Attributes** \\ \hline VGG & – & Multiple \(3\times 3\) sized kernels are used instead of large-sized kernels \\  & & which enables it to learn more complex features at a lower cost. \\  & – & The \(3\times 3\) kernels preserve finer-level properties of the image. \\  & – & Huge computation is needed both in terms of space and time. \\  & – & The large width of convolutional layers makes it inefficient. \\ \hline ResNet & – & It uses an identity skip connection that skips one or more layers and \\  & – & reuses the feature maps from previous layers. \\  & – & Faster learning is achieved by skipping the layers in the network. \\  & – & It handles the vanishing-gradient problem. \\ \hline DenseNet & – & It achieves good accuracy with a smaller amount of computation. \\  & – & It reuses the learned features in subsequent layers which reduces \\  & – & the its number of parameters. \\  & – & It provide better solution to vanishing-gradient problem. \\ \hline Inception & – & It uses different size convolutions to capture details at varied scales. \\  & – & To reduce the computational cost, the number of input channels \\  & & is limited by adding \(1\times 1\) convolution before performing large \\  & – & Smart factorization methods reduce the convolution cost. \\  & – & It replaces the FC layers with global average pooling layers without \\  & & compromising the accuracy, resulting in few parameters. \\ \hline Xception & – & It achieves the high accuracy without any intermediate activation. \\  & – & It also uses residual or identity skip connections to increase accuracy. \\  & – & It has the same number of parameters as Inception V3, the performance improves due to efficient use of model parameters. \\ \hline SqueezeNet & – & It requires small memory and less bandwidth over the network. \\  & – & Model parameters are reduced by using squeeze layers which perform convolution by using a \(1\times 1\) filter. \\  & – & Delayed down-sampling in a network maximizes accuracy with \\  & & limited model parameters. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of attributes of the compared deep learning models. Before introducing the proposed DL model for efficient malaria detection, we instigate a data pre-processing step that is effective for improving the quality of the images. During data acquisition, the images may get polluted with various types of noise, e.g., camera angle and microscope positions. Different noise removal methods have been proposed in the literature to eliminate such kinds of noise from the images. These methods include simple blur operators such as averaging filter and non-linear filters e.g., median filter. In our case, the RBC scans are of low resolution and contain important information about the parasite presentation that could be degraded or lost if simple blurring methods are used. We need an image denoising method that removes the noise from the image, preserving the structural information present in the image. We found the bilateral filter [71] to be quite effective in this scenario, as shown in [11]. In conventional image blurring methods, in the computation of the new pixel value, each pixel contributes based on its distance from the filter center. The filter weights in the bilateral filter consider both the spatial distance of the pixel from the filter and the pixel color/intensity range differences. The former factor introduces the blur in the image and the latter tries to preserve the structural information in the image. Let \(I\) be an input image of size \(M\times N\) which is subject to bilateral fileting of size size \((2d+1)\times(2d+1)\).