1.2. Wholeslide images for computer-aided malaria infection classification

Among recent works on computer-aid diagnosis, two types of images have found prevalent use: light microscopic images and wholeslide images. The former has been in existence since a longer time frame compared to the latter, which has come into popular adoption recently. Because of recent advancements in computing power, improved cloud-based services and robust algorithms have enabled the widespread use of wholeslide images. For conventional light microscopy, the patient tissue image is acquired by means of incision and then examined under a light microscope. A diagnostic conclusion is arrived upon based interpretation of multiple slide samples [6]. This type of examination does not provide a good sensitivity and specificity for malaria diagnosis [7]. With an aim to standardize slide interpretations, wholeslide images were introduced. The wholeslide image is obtained by scanning an entire slide in one pass. The final image consists of several component images obtained by scanning the areas under the respective fields of sight of the microscope and stitched together. The most widely used methods of scanning include tile scanning and line scanning. In tile scan, the component images are obtained in the form of 512 512 tiles. In the line scan method, the component images are generated in a strip-scan fashion.

infections in 2015 [1]. Endemic regions with widespread disease include Africa and South-East Asia. In these and other parts of the world where malaria mortality is significant, necessary resources such as reliable prevention, healthcare, and hygiene are far from adequate [1]. In most cases, the only available method of malaria diagnosis is manual examination of the microscopic slide [2]. In order to provide reliable diagnosis, extensive experience and training are required. Unfortunately, such specialized human resources are very often limited in rural areas where malaria has a marked predominance. Also, manual microscopy is subjective and suffers from a lack of standardization. This problem is further exacerbated by the large size of

The issues associated with manual diagnosis present the case for automation of the malaria diagnosis process. The automation of the diagnosis process will ensure accurate diagnosis of the disease and hence holds the promise of delivering reliable health-care to resource-scarce areas. Hence, rural areas suffering from lack of specialized infrastructure and trained manpower can benefit greatly from automated diagnosis. Automating the diagnosis of malaria involves adapting the methods, expertise, practices, and knowledge of conventional microscopy to a computerized system structure [3]. Early detection of malaria is essential for ensuring proper diagnosis and increasing chances of cure. In consideration of the severity and the number of fatalities claimed by this disease, it is rational to accept potential small implementation errors introduced by an automated system. An automated system consists of streamlined image processing techniques for initial filtering and segmentation and suite of pattern recognition and/or machine learning algorithms directed toward robustly recognizing infected cells in a light or wholeslide microscopic image [4]. Previous studies have shown that the degree of agreement between clinicians on the severity of the disease in a given patent's sample is very low. Hence, a computer-assisted system as a decision support system can be paramount to faster and reliable diagnosis. It can help provide a benchmark and standardized

microscopic wholeslide images, which require a lengthy scanning.

1.1. The need for an automated malaria diagnosis process

160 Machine Learning - Advanced Techniques and Emerging Applications

way of measuring the degree of infection of the disease [5].

1.2. Wholeslide images for computer-aided malaria infection classification

Among recent works on computer-aid diagnosis, two types of images have found prevalent use: light microscopic images and wholeslide images. The former has been in existence since a longer time frame compared to the latter, which has come into popular adoption recently. Because of recent advancements in computing power, improved cloud-based services and robust algorithms have enabled the widespread use of wholeslide images. For conventional light microscopy, the patient tissue image is acquired by means of incision and then examined under a light microscope. A diagnostic conclusion is arrived upon based interpretation of multiple slide samples [6]. This type of examination does not provide a good sensitivity and specificity for malaria diagnosis [7]. With an aim to standardize slide interpretations, wholeslide images were introduced. The wholeslide image is obtained by scanning an entire slide in one pass. The final image consists of several component images obtained by scanning the areas under the respective fields of sight of the microscope and stitched together. The most The file sizes of the wholeslide images are governed by the objective of the lens while scanning. Wholeslide images scanned 40 objective give rise to substantially large file size, for instance, approximately 2 GB. Magnification beyond the maximum level can result in pixelation [8]. Wholeslide images can be decomposed into a pyramid structure of different resolutions. The image at each magnification level is broken down into smaller constituent tiles and stored in respective folders. The image pyramid allows for real-time viewing of wholeslide images. The zoom levels are precalculated and stored in the metadata associated with the file [9]. Each tile can be viewed and analyzed individually. Figure 1 shows an illustration [10]. The process of

Figure 1. (a) Image pyramid of the DeepZoom structure. Example image tiles at various DeepZoom pyramid resolutions (b) Level 14, (c) Level 15, and (d) Level 16. Each tile image is 258 258, and magnification is 100.

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 can be worked on individually [11].

datasets are not sufficiently large, one of the major challenges with training deep CNNs is to deal with the risk of overfitting. When training error is low but the test error is high, the model fails to learn a proper generalization of knowledge contained in data [18]. There are ways to regularize the deep network, such as randomized pruning of excessive connectivity, but

Classification of Malaria-Infected Cells Using Deep Convolutional Neural Networks

http://dx.doi.org/10.5772/intechopen.72426

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In this chapter, we present some of our recent progresses on highly accurate classification of malaria-infected cells using deep convolutional neural networks. We will discuss the procedures of compiling a pathologists-curated image dataset for training deep neural network, as well as data augmentation methods used to significantly increase the size of the dataset, in light of the overfitting problem associated with training deep convolutional neural networks. In the next section, we describe image processing methods used for segmentation of red blood

2. Cell image pre-processing and compilation of dataset for deep learning

The images used in this work were wholeslide images provided in the PEIR-VM repository built by the University of Alabama in Birmingham. The original whole slide image data contain significant amount of redundant information. In order to achieve good classification accuracy, image segmentation and de-noising are needed to extract only blood cells and remove those redundant image pixels simultaneously. Several effective image processing

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

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

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.

techniques were used to accurately segment tiles into individual cells.

tiles (29%) were marked as suspect and require further analysis.

dots and holes to finally obtain the individual cell samples.

overfitting is still a threat with small image datasets, especially with unbiased data.

cells from wholeslide images.

2.1. Image pre-processing tasks
