Malaria detection using deep residual networks with mobile microscopy

Pattanaik, Priyadarshini Adyasha; Mittal, Mohit; Khan, Mohammad Zubair; Panda, Surya Narayan

doi:10.1016/j.jksuci.2020.07.003

Cited by 41 publications

(22 citation statements)

References 12 publications

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“…The estimate of f ( Iv,{P i }) notifies the outstanding mapping to be understood. The capacity to avert signal mitigation through the conversion of many stacked nonlinearities is one advantage of the residual link [ 71 ], as shown in Figure 9 .…”

Section: Implementation Detailsmentioning

confidence: 99%

Analyzing Malaria Disease Using Effective Deep Learning Approach

2020

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Medical tools used to bolster decision-making by medical specialists who offer malaria treatment include image processing equipment and a computer-aided diagnostic system. Malaria images can be employed to identify and detect malaria using these methods, in order to monitor the symptoms of malaria patients, although there may be atypical cases that need more time for an assessment. This research used 7000 images of Xception, Inception-V3, ResNet-50, NasNetMobile, VGG-16 and AlexNet models for verification and analysis. These are prevalent models that classify the image precision and use a rotational method to improve the performance of validation and the training dataset with convolutional neural network models. Xception, using the state of the art activation function (Mish) and optimizer (Nadam), improved the effectiveness, as found by the outcomes of the convolutional neural model evaluation of these models for classifying the malaria disease from thin blood smear images. In terms of the performance, recall, accuracy, precision, and F1 measure, a combined score of 99.28% was achieved. Consequently, 10% of all non-dataset training and testing images were evaluated utilizing this pattern. Notable aspects for the improvement of a computer-aided diagnostic to produce an optimum malaria detection approach have been found, supported by a 98.86% accuracy level.

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Section: Implementation Detailsmentioning

confidence: 99%

Analyzing Malaria Disease Using Effective Deep Learning Approach

2020

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“…Nonetheless, this work still lean towards an empirical analysis rather than a developmental approach about how recent DCNN models, specifically EfficientNet, in malaria parasite detection and classification from blood smears performs. [22] 97.37 96.99 97.75 DBN [23] 96.21 97.60 95.92 Custom-CNN [24] 98.47 97.06 98.50 ResNet50 [26] 95.9 94.7 97.2 MM-ResNet [28] 98.08 95.38 98.30 Novel CAD Scheme [29] 89.10 93.90 83.10…”

Section: Discussionmentioning

confidence: 99%

“…With that said, their model preserves the small to large feature samples across the network without drastic saturation and achieves better information handling than the baseline model. Together with the parallelism technique to train multiple inputs, they trained their model with 1000 epochs and achieved a remarkable 98.08% accuracy [29].…”

Section: Related Workmentioning

confidence: 99%

Empirical Analysis of a Fine-Tuned Deep Convolutional Model in Classifying and Detecting Malaria Parasites from Blood Smears

2021

KSII TIIS

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In this work, we empirically evaluated the efficiency of the recent EfficientNetB0 model to identify and diagnose malaria parasite infections in blood smears. The dataset used was collected and classified by relevant experts from the Lister Hill National Centre for Biomedical Communications (LHNCBC). We prepared our samples with minimal image transformations as opposed to others, as we focused more on the feature extraction capability of the EfficientNetB0 baseline model. We applied transfer learning to increase the initial feature sets and reduced the training time to train our model. We then fine-tuned it to work with our proposed layers and retrained the entire model to learn from our prepared dataset. The highest overall accuracy attained from our evaluated results was 94.70% from fifty epochs and followed by 94.68% within just ten. Additional visualization and analysis using the Gradientweighted Class Activation Mapping (Grad-CAM) algorithm visualized how effectively our fine-tuned EfficientNetB0 detected infections better than other recent state-of-the-art DCNN models. This study, therefore, concludes that when fine-tuned, the recent EfficientNetB0 will generate highly accurate deep learning solutions for the identification of malaria parasites in blood smears without the need for stringent pre-processing, optimization, or data augmentation of images.

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“…We further highlight the acquisitions in the study of deep learning and its applications in the analysis of the medical image [41]. You can easily identify references to image labeling and annotation, developing new deep learning models with increased performance, and new approaches to medical image processing: [155].…”

Section: Applications In Medicine and The Performance Of DL Models Depending On The Therapeutic Areas In Which They Were Usedmentioning

confidence: 99%

Deep Learning Application for Analyzing of Constituents and Their Correlations in the Interpretations of Medical Images

et al. 2021

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The need for time and attention, given by the doctor to the patient, due to the increased volume of medical data to be interpreted and filtered for diagnostic and therapeutic purposes has encouraged the development of the option to support, constructively and effectively, deep learning models. Deep learning (DL) has experienced an exponential development in recent years, with a major impact on interpretations of the medical image. This has influenced the development, diversification and increase of the quality of scientific data, the development of knowledge construction methods and the improvement of DL models used in medical applications. All research papers focus on description, highlighting, classification of one of the constituent elements of deep learning models (DL), used in the interpretation of medical images and do not provide a unified picture of the importance and impact of each constituent in the performance of DL models. The novelty in our paper consists primarily in the unitary approach, of the constituent elements of DL models, namely, data, tools used by DL architectures or specifically constructed DL architecture combinations and highlighting their “key” features, for completion of tasks in current applications in the interpretation of medical images. The use of “key” characteristics specific to each constituent of DL models and the correct determination of their correlations, may be the subject of future research, with the aim of increasing the performance of DL models in the interpretation of medical images.

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Malaria detection using deep residual networks with mobile microscopy

Cited by 41 publications

References 12 publications

Analyzing Malaria Disease Using Effective Deep Learning Approach

Analyzing Malaria Disease Using Effective Deep Learning Approach

Empirical Analysis of a Fine-Tuned Deep Convolutional Model in Classifying and Detecting Malaria Parasites from Blood Smears

Deep Learning Application for Analyzing of Constituents and Their Correlations in the Interpretations of Medical Images

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