Mobile Hardware Based Implementation of a Novel, Efficient, Fuzzy Logic Inspired Edge Detection Technique for Analysis of Malaria Infected Microscopic Thin Blood Images

Bias, Stephen; Reni, Saumya Kareem; Kale, I.

doi:10.1016/j.procs.2018.10.187

Cited by 8 publications

(5 citation statements)

References 5 publications

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“…Before the emergence of deep learning techniques, malaria parasite detection in images relied on classical methods involving multiple steps: image preprocessing, object detection or segmentation, feature extraction, and classification. Techniques like mathematical morphology for preprocessing and segmentation [31,32], along with handcrafted features [33,34], have been used to train machine learning classifiers. The landscape of computer vision approaches for malaria parasite detection underwent a significant transformation with the introduction of AlexNet's Convolutional Neural Network (CNN) [39], marking a paradigm shift.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Existing literature only addresses the classification problem without considering the detection problem. Additionally, considerable emphasis has been placed on developing mobile devices to facilitate cost-effective and rapid malaria diagnosis, particularly in underdeveloped regions where access to more-expensive laboratory facilities is limited [34].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Of course, this is valid under the assumption that pathologists take crops manually or that detection systems provide perfect crops. However, this assumption is not verified in real-world application scenarios because the systems are fully automated, and therefore, the crops cannot always be accurate or perfectly centered [31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

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YOLO-PAM: Parasite-Attention-Based Model for Efficient Malaria Detection

Zedda,

Loddo,

Di Ruberto

2023

J. Imaging

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Malaria is a potentially fatal infectious disease caused by the Plasmodium parasite. The mortality rate can be significantly reduced if the condition is diagnosed and treated early. However, in many underdeveloped countries, the detection of malaria parasites from blood smears is still performed manually by experienced hematologists. This process is time-consuming and error-prone. In recent years, deep-learning-based object-detection methods have shown promising results in automating this task, which is critical to ensure diagnosis and treatment in the shortest possible time. In this paper, we propose a novel Transformer- and attention-based object-detection architecture designed to detect malaria parasites with high efficiency and precision, focusing on detecting several parasite sizes. The proposed method was tested on two public datasets, namely MP-IDB and IML. The evaluation results demonstrated a mean average precision exceeding 83.6% on distinct Plasmodium species within MP-IDB and reaching nearly 60% on IML. These findings underscore the effectiveness of our proposed architecture in automating malaria parasite detection, offering a potential breakthrough in expediting diagnosis and treatment processes.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

YOLO-PAM: Parasite-Attention-Based Model for Efficient Malaria Detection

Zedda,

Loddo,

Di Ruberto

2023

J. Imaging

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“…They aim to reduce the problems of manual analysis depicted in Section 1 and provide a more robust and standardised interpretation of blood samples while reducing the costs of diagnosis [ 15 , 21 , 22 ], mainly represented by CAD systems. They can be based on the combination of image processing and traditional machine learning techniques [ 23 , 24 , 25 ], and also deep learning approaches [ 16 , 26 , 27 , 28 ], especially after the proposal of AlexNet’s convolutional neural network (CNN) [ 29 ].…”

Section: Related Workmentioning

confidence: 99%

“…Finally, a recent focus has been posed on mobile devices, which enable a cheaper and quicker diagnosis in the underdeveloped areas of the world, where more expensive laboratories do not exist. As an example, Bias et al [ 24 ] realised an edge detection technique based on a novel histogram-based analysis, coupled with easily accessible hardware, focused on malaria-infected thin smear images.…”

Section: Related Workmentioning

confidence: 99%

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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