2.1. Image pre-processing tasks

examining a wholeslide image is termed as "virtual microscopy", since analysis and examination can be performed through compatible software virtually on the computer. The DeepZoom structure is favorable in terms of storage and transmission. This arrangement of the original wholeslide image allows for smooth loading and panning using multiresolution images. Initially upon loading, a low-resolution version of the image is displayed. The higher resolution details get blended into the image as they become available. Thus, while viewing the image in DeepZoom, the user experiences a blurred image to sharp image transition. In terms of transmission, the DeepZoom structure is bandwidth efficient. Since, initially, a coarse lowresolution version is transmitted, the bandwidth overhead is reduced. At each level, each tile

There has recently been an increasing amount of studies devoted to the application of computer vision and machine learning technologies to the automated diagnosis of malaria. Among the most recent related work [12–16], an automated analysis method was presented in [14] for detection and staging of red blood cells (RBCs) infected by the malaria parasite. In order to classify RBCs, three different types of machine learning algorithms were tested for prediction accuracy and speed as RBC classifiers. In [12], the authors built a low-cost automated digital microscope coupled with a set of computer vision and classification algorithms. Support vector machine (SVM) has been applied to detect malaria-infected cells using provided handcrafted features. In our prior work [17], we sought the best features from a set of 76 features organized into five categories extracted from the input data, in order to optimize SVM-based classification of wholeslide malarial smear images. We found that the binary SVM classifier yielded a superlative accuracy of 95.5% if the feature-selection is based on Kullback-Leibler distance. In contrast, deep learning has appeared as a genre of machine learning algorithms, which attempt to solve problems by learning abstraction in data following a stratified description paradigm based on non-linear transformation architectures. Recent advances in deep machine learning provide tools to automatically classify images and objects with (and occasionally exceeding) human-level accuracy. A key advantage of deep learning is its ability to perform semi-supervised or

Deep learning has found exciting new applications in biomedicine [18], genomic medicine [19], bioinformatics [20], and medical imaging analysis [21–28]. However, there has been very sparse work on applying deep learning methods to computer-assisted malaria infection detection. In [16] were described point-of-care diagnostics using microscopes and smartphones, where deep convolutional neural network (CNN) was employed to identify image patches suspected to contain malaria-infected RBCs. The detection accuracy is similar to the results achieved with deep learning [15], where a CNN (with three convolutional layers and two fully connected layers) achieved a precision of 95.31% using images from dedicated microscope cameras [16]. Nevertheless, deep learning methods typically involve the calculation of tens of thousands of parameters, which in turn require large training datasets that may not be readily available. Thus, many commonly used machine learning methods such as support vector machine can outperform deep learning methods when experimental data is scarce. When the

1.3. Classification of malaria-infected red blood cells using deep learning

can be worked on individually [11].

162 Machine Learning - Advanced Techniques and Emerging Applications

unsupervised feature extraction over massive datasets.

Most image tiles may easily be visualized as having no malaria-infected cells, so preselection of noninfected tiles can be used to significantly reduce overall processing runtime. Given the contrast between the darkly purple/blue-stained nuclei of malaria and the light pink color of normal cells, pixel color information is used for preliminary selection of "infected" tiles. In order to estimate the color of infected cells, we conducted statistical analysis on the collected cell pixels. The maximal and minimal RGB values of infected cells were selected as two thresholds for "suspect" tiles. Considering the risk of excluding infected cells, we expanded the selected RGB value range to include more tiles. In this work, 24,648 of the original 85,094 tiles (29%) were marked as suspect and require further analysis.

For the suspected tile, thresholding is performed on the binarized image using Otsu's method. An example is shown in Figure 2. We can see that noise not only exists in the image background but also inside RBCs. A series of morphological steps were applied to fill the isolated dots and holes to finally obtain the individual cell samples.

In our work, only RBCs will provide features in the wholeslide image to the following classification. Therefore, we only keep RBCs and remove everything else using a combination of morphological operations. After all RBCs are processed, we then obtain all clean RBC samples for further classification. Figure 3 shows some normal and infected RBC samples.

3. Convolution neural network

4. Image data augmentation

subsampling (pooling) layers (S2 and S4), and two fully connected layers.

network used for classification of the red blood cell images.

Convolutional neural network is an artificial neural network inspired by the animal visual system [29]. Convolutional layer, pooling layer, and fully connection layer are the three main types of layers used to construct the CNN architecture. Compared to traditional neural networks, CNNs can extract features without losing much spatial correlations of the input. Each layer consists of neurons that have learnable weights and biases. The optimal model is achieved after feeding data into the network and minimizing the loss function at the top layer. Several different architectures of CNN have been proposed. In this work, we used LeNet-5. LeNet-5 [30] was first used in handwritten digit recognition and achieved an impressive error rate as low as 0.8%. Figure 4 shows the architecture of the LeNet-5 convolutional neural

Classification of Malaria-Infected Cells Using Deep Convolutional Neural Networks

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One of the major challenges of the research is that the current image dataset is still too small, which could lead to overfitting when used for training deep convolutional neural network. To this end, we consider data augmentation. More similar images can be added to the dataset by applying to the existing images operations such as rotation, translation, flip, zoom, and color perturbations. Other methods include data augmentation in the spatial domain by learning the statistical models of data transformation [31], as well as data augmentation through interpolation and extrapolation in the feature domain ([32, 33]). In the following, we present our work in augmenting the image dataset of the red blood cells and discuss the impact of the data augmentation on the image classification accuracies using deep convolutional neural network.

The set of infected red blood cell images has 800 images, each with size of 50503 (for red, green, and blue channels). Only the red channel pixel values were used. Since we want to

Figure 4. LeNet-5 convolutional neural network architecture. There are two convolution layers (C1 and C3), two

Figure 2. Steps of image pre-processing. (a) An image tile of interest; (b) Otsu thresholded image; and (c) morphologically filled image.

Figure 3. Some example segmented red blood cell images. (Upper row) normal cells and (lower row) infected cells.

#### 2.2. Construction of an image dataset

There is no sufficiently large, high-quality image dataset of pathologically annotated cell images available to fully train multiple-layer neural networks. The only reasonably large, publicly available dataset in [16] we are aware of contains only 2703 images. However, these images were taken from thick blood smears, showing blurry patches rather than extractable RBCs found in high-resolution wholeslide images scanned from thin blood smears. Therefore, we worked with a team of pathologists to construct a dataset. After the data preprocessing, we randomly selected a large number of cell images and provided them to pathologists at the University of Alabama at Birmingham. The entire whole slide image dataset have been divided into four segments evenly. Each of four pathologists is assigned with two segments so that each cell image will be viewed and labeled by at least two experienced pathologists. One cell image can only be considered as infected and included in our final dataset if all the reviewers mark it positively whereas it will be excluded otherwise. The same selection rule also applies to the normal cells in our dataset.
