Performance analysis of machine learning and deep learning architectures for malaria detection on cell images

Plasmodium malaria is a parasitic protozoan that causes malaria in humans. Computer aided detection of Plasmodium is a research area attracting great interest. In this paper, we study the performance of various machine learning and deep learning approaches for the detection of Plasmodium on cell images from digital microscopy. We make use of a publicly available dataset composed of 27,558 cell images with equal instances of parasitized (contains Plasmodium) and uninfected (no Plasmodium) cells. We randomly split the dataset into groups of 80% and 20% for training and testing purposes, respectively. We apply color constancy and spatially resample all images to a particular size depending on the classification architecture implemented. We propose a fast Convolutional Neural Network (CNN) architecture for the classification of cell images. We also study and compare the performance of transfer learning algorithms developed based on well-established network architectures such as AlexNet, ResNet, VGG-16 and DenseNet. In addition, we study the performance of the bag-of-features model with Support Vector Machine for classification. The overall probability of a cell image comprising Plasmodium is determined based on the average of probabilities provided by all the CNN architectures implemented in this paper. Our proposed algorithm provided an overall accuracy of 96.7% on the testing dataset and area under the Receiver Operating Characteristic (ROC) curve value of 0.994 for 2756 parasitized cell images. This type of automated classification of cell images would enhance the workflow of microscopists and provide a valuable second opinion.

[1]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Wen-Huang Cheng,et al.  Computer-aided classification of lung nodules on computed tomography images via deep learning technique , 2015, OncoTargets and therapy.

[3]  Russell C. Hardie,et al.  Analysis of various classification techniques for computer aided detection system of pulmonary nodules in CT , 2016, 2016 IEEE National Aerospace and Electronics Conference (NAECON) and Ohio Innovation Summit (OIS).

[4]  David M. Rubin,et al.  Automated image processing method for the diagnosis and classification of malaria on thin blood smears , 2006, Medical and Biological Engineering and Computing.

[5]  Mubashar Mushtaq,et al.  Ensemble classification of pulmonary nodules using gradient intensity feature descriptor and differential evolution , 2017, Cluster Computing.

[6]  Qinglin Zhao,et al.  Support for spot virtual machine purchasing simulation , 2018, Cluster Computing.

[7]  Chandan Chakraborty,et al.  Machine learning approach for automated screening of malaria parasite using light microscopic images. , 2013, Micron.

[8]  Russell C. Hardie,et al.  Optimized feature selection-based clustering approach for computer-aided detection of lung nodules in different modalities , 2017, Pattern Analysis and Applications.

[9]  Ashish Gupta,et al.  Using Deep Learning for Pulmonary Nodule Detection & Diagnosis , 2016, AMCIS.

[10]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[11]  Almabrok E. Essa,et al.  A Leaf Recognition Approach to Plant Classification Using Machine Learning , 2018, NAECON 2018 - IEEE National Aerospace and Electronics Conference.

[12]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[13]  George R Thoma,et al.  Understanding the learned behavior of customized convolutional neural networks toward malaria parasite detection in thin blood smear images , 2018, Journal of medical imaging.

[14]  Mahdieh Poostchi,et al.  Image analysis and machine learning for detecting malaria , 2018, Translational research : the journal of laboratory and clinical medicine.

[15]  L. Maloney,et al.  Color constancy: a method for recovering surface spectral reflectance , 1987 .

[16]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Russell C. Hardie,et al.  Skin Lesion Segmentation and Classification for ISIC 2018 Using Traditional Classifiers with Hand-Crafted Features , 2018, ArXiv.

[18]  Barath Narayanan Narayanan,et al.  Performance analysis of a computer-aided detection system for lung nodules in CT at different slice thicknesses , 2018, Journal of medical imaging.

[19]  Wenqing Sun,et al.  Computer aided lung cancer diagnosis with deep learning algorithms , 2016, SPIE Medical Imaging.

[20]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[21]  Hengyong Yu,et al.  Deep Learning for the Classification of Lung Nodules , 2016, ArXiv.

[22]  Ann Q. Gates,et al.  TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING , 2005 .

[23]  Y. Kawata,et al.  Computer-aided diagnosis for pulmonary nodules based on helical CT images , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).