A comparative analysis of deep learning architectures on high variation malaria parasite classification dataset

Rahman, Asifur; Zunair, Hasib; Reme, Tamanna Rahman; Rahman, Mahfuzur; Mahdy, M. R. C.

doi:10.1016/j.tice.2020.101473

Cited by 39 publications

(25 citation statements)

References 21 publications

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“…Subsequently, they further improved through an ensemble of CNNs [28]. Rahman et al [30] also exploited TL strategies using both natural and medical images and performed an extensive test of some off-the-shelf CNNs to realise a binary classification. Some other techniques not explored in this work are based on the combination of CNNextracted features and handcrafted ones [31][32][33] or the direct use of object detectors [34].…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…The work in [ 30 ] is the most similar to the approach here proposed. In particular, they compared different off-the-shelf networks for a binary classification using two datasets, one is the Malaria Parasite Image Database for Image Processing and Analysis (MP-IDB) [ 35 ] and another is composed of synthetic and medical images.…”

Section: Related Workmentioning

confidence: 99%

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An Empirical Evaluation of Convolutional Networks for Malaria Diagnosis

2022

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Malaria is a globally widespread disease caused by parasitic protozoa transmitted to humans by infected female mosquitoes of Anopheles. It is caused in humans only by the parasite Plasmodium, further classified into four different species. Identifying malaria parasites is possible by analysing digital microscopic blood smears, which is tedious, time-consuming and error prone. So, automation of the process has assumed great importance as it helps the laborious manual process of review and diagnosis. This work focuses on deep learning-based models, by comparing off-the-shelf architectures for classifying healthy and parasite-affected cells, by investigating the four-class classification on the Plasmodium falciparum stages of life and, finally, by evaluating the robustness of the models with cross-dataset experiments on two different datasets. The main contributions to the research in this field can be resumed as follows: (i) comparing off-the-shelf architectures in the task of classifying healthy and parasite-affected cells, (ii) investigating the four-class classification on the P. falciparum stages of life and (iii) evaluating the robustness of the models with cross-dataset experiments. Eleven well-known convolutional neural networks on two public datasets have been exploited. The results show that the networks have great accuracy in binary classification, even though they lack few samples per class. Moreover, the cross-dataset experiments exhibit the need for some further regulations. In particular, ResNet-18 achieved up to 97.68% accuracy in the binary classification, while DenseNet-201 reached 99.40% accuracy on the multiclass classification. The cross-dataset experiments exhibit the limitations of deep learning approaches in such a scenario, even though combining the two datasets permitted DenseNet-201 to reach 97.45% accuracy. Naturally, this needs further investigation to improve the robustness. In general, DenseNet-201 seems to offer the most stable and robust performance, offering as a crucial candidate to further developments and modifications. Moreover, the mobile-oriented architectures showed promising and satisfactory performance in the classification of malaria parasites. The obtained results enable extensive improvements, specifically oriented to the application of object detectors for type and stage of life recognition, even in mobile environments.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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An Empirical Evaluation of Convolutional Networks for Malaria Diagnosis

2022

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“…However, thick blood film is the most recommended slide preparation technique for malaria diagnosis, and the development of robust automated tools will be very significant to reduce the challenges related to manual microscopy. Most of the existing studies on malaria parasite detection or classification are done using thin smear blood films [40], [41], [42], [43], [44]. This may be due to the easiness of detecting infected and uninfected RBCs due to their bigger size.…”

Section: Related Workmentioning

confidence: 99%

Tile-Based Microscopic Image Processing For Malaria Screening Using Deep Learning Approach

Abdurahman

Fante

2022

Preprint

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Background: Manual microscopic examination remains the golden standard for malaria diagnosis. However, it is cumbersome and requires the experience of pathologists for accurate diagnosis. The shear workload and challenges involved in manual microscopy drives for alternative computer-aided diagnosis techniques. While the importance of computer-aided diagnosis is increasing at an enormous pace fostered by the advancement of deep learning algorithms, there are still challenges. Existing state-of-the-art (SOTA) deep learning-based object detection models suffer from effectively detecting small objects which are less represented on benchmark datasets and are affected by the loss of detailed spatial information due to in-network feature map downscaling. The problem even becomes harder when the input image is high resolution. This is due to the fact that existing SOTA models will not directly process high-resolution images limited by their low-resolution network input size. Methods: In this study, an effective and robust tile-based image processing approach was proposed to enhance malaria parasite detection performance of SOTA object detection models. Three YOLOV4 based object detectors were adopted considering their detection speed and accuracy. The proposed detection models were developed by using tiles generated from 1,780 high-resolution P. falciparum-infected thick smear microscopic images. External validation was performed to verify the detection accuracy and generalization ability of the proposed models on three datasets acquired from a different domain.Results: The best-performing model leveraging the proposed tile-based approach significantly outperforms its baseline method (Recall, [95.3%] vs [57%] and Average Precision, [87.1%] vs [76%]). In addition, the proposed method achieved state-of-the-art performance compared with previous research works which use different machine learning techniques on similar datasets.Conclusions: The obtained qualitative and empirical results show that the proposed method reveals a fundamental performance improvement for the detection of P. falciparum from thick smear microscopic images while maintaining real-time speed. Furthermore, the proposed method could have the potential to assist and reduce the workload of laboratory technicians in malaria-endemic remote areas in developing countries where there is a critical skill gap and shortage of experts.

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“…The microscopic system does not suffer from this shortcoming and it is considered to be efficient for malaria parasite diagnosis [ 8 ]. However, these techniques require the existence of a skilled microscopist [ 9 ]. Automated microscopic malaria parasite diagnosis includes segmentation of cells and classification of infected cells, and the acquisition of the microscopic blood smear images could be a powerful diagnosis method [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Barnacles Mating Optimizer with Deep Transfer Learning Enabled Biomedical Malaria Parasite Detection and Classification

Dutta

Mageswari²,

Gayathri

et al. 2022

Computational Intelligence and Neuroscience

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Biomedical engineering involves ideologies and problem-solving methods of engineering to biology and medicine. Malaria is a life-threatening illness, which has gained significant attention among researchers. Since the manual diagnosis of malaria in a clinical setting is tedious, automated tools based on computational intelligence (CI) tools have gained considerable interest. Though earlier studies were focused on the handcrafted features, the diagnostic accuracy can be boosted through deep learning (DL) methods. This study introduces a new Barnacles Mating Optimizer with Deep Transfer Learning Enabled Biomedical Malaria Parasite Detection and Classification (BMODTL-BMPC) model. The presented BMODTL-BMPC model involves the design of intelligent models for the recognition and classification of malaria parasites. Initially, the Gaussian filtering (GF) approach is employed to eradicate noise in blood smear images. Then, Graph cuts (GC) segmentation technique is applied to determine the affected regions in the blood smear images. Moreover, the barnacles mating optimizer (BMO) algorithm with the NasNetLarge model is employed for the feature extraction process. Furthermore, the extreme learning machine (ELM) classification model is employed for the identification and classification of malaria parasites. To assure the enhanced outcomes of the BMODTL-BMPC technique, a wide-ranging experimentation analysis is performed using a benchmark dataset. The experimental results show that the BMODTL-BMPC technique outperforms other recent approaches.

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A comparative analysis of deep learning architectures on high variation malaria parasite classification dataset

Cited by 39 publications

References 21 publications

An Empirical Evaluation of Convolutional Networks for Malaria Diagnosis

An Empirical Evaluation of Convolutional Networks for Malaria Diagnosis

Tile-Based Microscopic Image Processing For Malaria Screening Using Deep Learning Approach

Barnacles Mating Optimizer with Deep Transfer Learning Enabled Biomedical Malaria Parasite Detection and Classification

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