1. Introduction

Malaria is a widespread disease that has claimed millions of lives all over the world. According to the World Health Organization, approximately 438,000 deaths result from 214 million

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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 microscopic wholeslide images, which require a lengthy scanning.

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

Classification of Malaria-Infected Cells Using Deep Convolutional Neural Networks

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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.

component images are generated in a strip-scan fashion.
