Automatic Detection of Malaria Parasites Using Unsupervised Techniques

Mohanty, Itishree; Pattanaik, Priyadarshini Adyasha; Swarnkar, Tripti

doi:10.1007/978-3-030-00665-5_5

Cited by 17 publications

(10 citation statements)

References 8 publications

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“…For the augmentation experiment, augmentation as per the Table 1 was applied first. While performing 64 × 64 augmentation experiment, original image was resized to (64, 64) before applying augmentation where augmentation was performed after resizing original image into (32,32) for the experiment of 32 × 32 augmentation. In both experiments, data was fed into the model according to batch size 32.…”

Section: General Trainingmentioning

confidence: 99%

“…In both experiments, data was fed into the model according to batch size 32. For the without augmentation training, plain data was fed into the model according to batch size 64 after resizing the original image to (64,64) or (32,32) as per the experiment requirement. Weights and biases of all trainable parameters were randomly initialized at the beginning of training.…”

Section: General Trainingmentioning

confidence: 99%

“…Cell segmentation is a well researched area and good performance has already been reported in multiple studies [27][28][29][30]. Previous works that concentrated on the classification of infected cells involve tools and techniques from image processing [31][32][33], computer vision [34][35][36] and machine learning [37][38][39]. However, solutions for classification of infected cells, which are both accurate and computationally efficient, have not been studied to the best of our knowledge.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Based Automatic Malaria Parasite Detection from Blood Smear and Its Smartphone Based Application

et al. 2020

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Malaria is a life-threatening disease that is spread by the Plasmodium parasites. It is detected by trained microscopists who analyze microscopic blood smear images. Modern deep learning techniques may be used to do this analysis automatically. The need for the trained personnel can be greatly reduced with the development of an automatic accurate and efficient model. In this article, we propose an entirely automated Convolutional Neural Network (CNN) based model for the diagnosis of malaria from the microscopic blood smear images. A variety of techniques including knowledge distillation, data augmentation, Autoencoder, feature extraction by a CNN model and classified by Support Vector Machine (SVM) or K-Nearest Neighbors (KNN) are performed under three training procedures named general training, distillation training and autoencoder training to optimize and improve the model accuracy and inference performance. Our deep learning-based model can detect malarial parasites from microscopic images with an accuracy of 99.23% while requiring just over 4600 floating point operations. For practical validation of model efficiency, we have deployed the miniaturized model in different mobile phones and a server-backed web application. Data gathered from these environments show that the model can be used to perform inference under 1 s per sample in both offline (mobile only) and online (web application) mode, thus engendering confidence that such models may be deployed for efficient practical inferential systems.

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Section: General Trainingmentioning

confidence: 99%

Section: General Trainingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Learning Based Automatic Malaria Parasite Detection from Blood Smear and Its Smartphone Based Application

et al. 2020

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“…Some other researchers [ 36 , 37 , 38 ] worked with vector quantizer based classifiers to improve compression performance. On the other hand, researchers use neural network [ 39 , 40 , 41 , 42 ] for joint classification/compression. A classifier based on wavelet and Fourier descriptor features was employed in [ 39 ] to promote lossless image compression.…”

Section: Introductionmentioning

confidence: 99%

“…Singular Value Decomposition (SVD) was used in [ 41 ] as compression method that can reduce the size of fingerprint images, while improving the classification accuracy. Two unsupervised data reduction techniques, Autoencoder and self-organizing maps, were compared in [ 42 ] to identify malaria from blood smear images.…”

Section: Introductionmentioning

confidence: 99%

Impact of Misclassification Rates on Compression Efficiency of Red Blood Cell Images of Malaria Infection Using Deep Learning

Dong

Pan

2019

Entropy

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Malaria is a severe public health problem worldwide, with some developing countries being most affected. Reliable remote diagnosis of malaria infection will benefit from efficient compression of high-resolution microscopic images. This paper addresses a lossless compression of malaria-infected red blood cell images using deep learning. Specifically, we investigate a practical approach where images are first classified before being compressed using stacked autoencoders. We provide probabilistic analysis on the impact of misclassification rates on compression performance in terms of the information-theoretic measure of entropy. We then use malaria infection image datasets to evaluate the relations between misclassification rates and actually obtainable compressed bit rates using Golomb–Rice codes. Simulation results show that the joint pattern classification/compression method provides more efficient compression than several mainstream lossless compression techniques, such as JPEG2000, JPEG-LS, CALIC, and WebP, by exploiting common features extracted by deep learning on large datasets. This study provides new insight into the interplay between classification accuracy and compression bitrates. The proposed compression method can find useful telemedicine applications where efficient storage and rapid transfer of large image datasets is desirable.

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