Evaluations of deep convolutional neural networks for automatic identification of malaria infected cells

Dong, Yuhang; Jiang, Zhuocheng; Shen, Hongda; Pan, W. David; Williams, Lance A.; Reddy, V.; Benjamin, William H.; Bryan, Allen W.

doi:10.1109/bhi.2017.7897215

Cited by 179 publications

(100 citation statements)

References 11 publications

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“…Previous applications of CNNs to cell classification have focused on classification of the current cell state from an image [11,12,13,14,15,16,17]. In contrast, here, we focused on images of dynamic cell movement and demonstrated that CNNs can prospectively predict the future direction of cell movement with high accuracy.…”

Section: Discussionmentioning

confidence: 72%

“…For general visual recognition tasks, CNNs have substantially outperformed conventional machine learning methods with hand-crafted features [7,8] , and they have been applied successfully to biological imaging [9,10]. In cell classification, use of CNNs has produced impressive results [11,12,13,14,15,16,17] : e.g., the classification of abnormal morphology in MFC-7 breast cancer cells [13], the classification of cervical cells in cytology images [15]; the identification of malariainfected cells [16]; and the automatic classification of Hep-2 (human epithelial-2) cell staining patterns [17].…”

Section: Introductionmentioning

confidence: 99%

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Predicting the future direction of cell movement with convolutional neural networks

Nishimoto

Tokuoka

Yamada

et al. 2018

Preprint

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Image-based deep learning systems, such as convolutional neural networks (CNNs), have recently been applied to cell classification, producing impressive results; however, application of CNNs has been confined to classification of the current cell state from the image. Here, we focused on dynamic cell movement where current and/or past cell shape can influence the future cell fate. We demonstrate that CNNs prospectively predicted the future direction of cell movement with high accuracy from a single image patch of a cell at a certain time. Furthermore, by visualizing the image features that were learned by the CNNs and contributed to their prediction, we could identify morphological features, e.g., the protrusions and trailing edge that have been experimentally reported to determine the direction of cell movement. Our results indicate that CNNs have the potential to predict the future cell fate from current cell shape, and can be used to automatically identify those morphological features that influence future cell fate.

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Section: Discussionmentioning

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Predicting the future direction of cell movement with convolutional neural networks

Nishimoto

Tokuoka

Yamada

et al. 2018

Preprint

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“…When medical image data can be easily obtained, it opens the possibility to explore with deep learning algorithm. Several studies [31,32,33] in the topic of malaria identification began to use deep learning, but the image used was still from thin blood smear.…”

Section: Resultsmentioning

confidence: 99%

Detection of malaria parasites in thick blood smear: A review

Azif

Nugroho²,

Wibirama³

2018

CST

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Based on data from World Health Organization, in 2015, there are 90% of deaths caused by malaria disease in Africa, Southeast Asia and countries of eastern Mediterranean. It makes the malaria become one of the most dangerous diseases that often leads to death. To support the diagnosis of malaria, early detection of plasmodium parasite is needed. Recently, malaria diagnosis process can be done with the help of computer, or often referred to as Computer Aided Diagnosis (CAD). By utilizing the digital image from the blood staining process, digital image processing can be performed to detect the presence of malaria parasite. There are 2 types of blood smear images that can be used in the malaria diagnosis process, namely, thin blood smear images and thick blood smear images. This paper provides a review of the techniques and methods used in the diagnosis of computer-assisted malaria using thick blood smear images as a diagnostic material.

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“…More recently, by using deep-learning models such as convolutional neural network (CNN), the step of hand-engineered feature selection is not needed, CNN training performs feature extraction jointly to the training. Moreover, as in many field, deep-learning methods outperform other machine learning methods [12]. Also, deep-learning architectures introduced the concept of transfer learning, where pre-trained models are either fine-tuned on the underlying data or used as feature extractors to aid in visual recognition tasks [13].…”

Section: Introductionmentioning

confidence: 99%

Classification and Generation of Microscopy Images With Plasmodium Falciparum Via Artificial Neural Networks

Ramarolahy

Gyasi

Crimi³

2020

Preprint

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Background: Recent studies use machine-learning techniques to detect parasites in microscopy images automatically. However, these tools are trained and tested in specific datasets. Indeed, even if over-fitting is avoided during the improvements of computer vision applications, large differences are expected. Differences might be related to settings of camera (exposure, white balance settings, etc) and different blood film slides preparation. Moreover, generative adversial networks offer new opportunities in microscopy: data homogenization, and increase of images in case of imbalanced or small sample size. Methods: Taking into consideration all those aspects, in this paper, we describe a more complete view including both detection and generating synthetic images: i) an automated detection used to detect malaria parasites on stained blood smear images using machine learning techniques testing several datasets. ii) investigate transfer learning and further testing in different unseen datasets having different staining, microscope, resolution, etc. iii) a generative approach to create synthetic images which can deceive experts. Results: The tested architecture achieved 0.98 and 0.95 area under the ROC curve in classifying images with respectively thin and thick smear. Moreover, the generated images proved to be very similar to the original and difficult to be distinguished by an expert microscopist, which identified correcly the real data for one dataset but had 50\% misclassification for another dataset of images. Conclusion: The proposed deep-learning architecture performed well on a classification task for malaria parasites classification. The automated detection for malaria can help the technician to reduce their work and do not need any presence of experts. Moreover, generative networks can also be applied to blood smear images to generate useful images for microscopists. Opening new ways to data augmentation, translation and homogenization.

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Evaluations of deep convolutional neural networks for automatic identification of malaria infected cells

Cited by 179 publications

References 11 publications

Predicting the future direction of cell movement with convolutional neural networks

Predicting the future direction of cell movement with convolutional neural networks

Detection of malaria parasites in thick blood smear: A review

Classification and Generation of Microscopy Images With Plasmodium Falciparum Via Artificial Neural Networks

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