107 research outputs found
A Robust Segmentation for Malaria Parasite Detection of Thick Blood Smear Microscopic Images
Parasite Detection on thick blood smears is a critical step in Malaria diagnosis. Most of the thick blood smear microscopic images have the following characteristics: high noise, a similar intensity between background and foreground, and the presence of artifacts. This situation makes the detection process becomes complicated. In this paper, we proposed a robust segmentation technique for malaria parasite detection of microscopic images obtained from various endemic places in Indonesia. The proposed method includes pre-processing, blood component segmentation using intensity slicing and morphological operation, blood component classification utilising rule based on properties of parasite candidates, and parasite candidate formation. The performance was evaluated on 30 thick blood smear microscopic images. The experimental results showed that the proposed segmentation method was robust to the different condition of image and histogram. It reduced the misclassification error and relative foreground error by 2.6% and 45.5%, respectively. Properties addition to blood component classification increased the system precision. Average of precision, recall, and F-measure of the proposed method were all 86%. It is proven that the proposed method is appropriate to be used for malaria parasites detection
Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review
Malaria is an infectious disease caused by parasites of the genus Plasmodium spp. It is transmitted to humans by the bite of an infected female Anopheles mosquito. It is the most common disease in resource-poor settings, with 241 illion malaria cases reported in 2020 according to the World Health Organization. Optical microscopy examination of blood smears is the gold standard technique for malaria diagnosis; however, it is a time-consuming method and a well-trained microscopist is needed to perform the microbiological diagnosis. New techniques based on digital imaging analysis by deep learning and artificial intelligence methods are a challenging alternative tool for the diagnosis of infectious diseases. In particular, systems based on Convolutional Neural Networks for image detection of the malaria parasites emulate the microscopy visualization of an expert. Microscope automation provides a fast and low-cost diagnosis, requiring less supervision. Smartphones are a suitable option for microscopic diagnosis, allowing image capture and software identification of parasites. In addition, image analysis techniques could be a fast and optimal solution for the diagnosis of malaria, tuberculosis, or Neglected Tropical Diseases in endemic areas with low resources. The implementation of automated diagnosis by using smartphone applications and new digital imaging technologies in low-income areas is a challenge to achieve. Moreover, automating the movement of the microscope slide and image autofocusing of the samples by hardware implementation would systemize the procedure. These new diagnostic tools would join the global effort to fight against pandemic malaria and other infectious and poverty-related diseases.The project is funded by the Microbiology Department of Vall
d’Hebron Universitary Hospital, the Cooperation Centre of the
Universitat Politècnica de Catalunya (CCD-UPC) and the
Probitas FoundationPostprint (published version
Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools : A review
Malaria is an infectious disease caused by parasites of the genus Plasmodium spp. It is transmitted to humans by the bite of an infected female Anopheles mosquito. It is the most common disease in resource-poor settings, with 241 million malaria cases reported in 2020 according to the World Health Organization. Optical microscopy examination of blood smears is the gold standard technique for malaria diagnosis; however, it is a time-consuming method and a well-trained microscopist is needed to perform the microbiological diagnosis. New techniques based on digital imaging analysis by deep learning and artificial intelligence methods are a challenging alternative tool for the diagnosis of infectious diseases. In particular, systems based on Convolutional Neural Networks for image detection of the malaria parasites emulate the microscopy visualization of an expert. Microscope automation provides a fast and low-cost diagnosis, requiring less supervision. Smartphones are a suitable option for microscopic diagnosis, allowing image capture and software identification of parasites. In addition, image analysis techniques could be a fast and optimal solution for the diagnosis of malaria, tuberculosis, or Neglected Tropical Diseases in endemic areas with low resources. The implementation of automated diagnosis by using smartphone applications and new digital imaging technologies in low-income areas is a challenge to achieve. Moreover, automating the movement of the microscope slide and image autofocusing of the samples by hardware implementation would systemize the procedure. These new diagnostic tools would join the global effort to fight against pandemic malaria and other infectious and poverty-related diseases
Malaria Parasitic Detection using a New Deep Boosted and Ensemble Learning Framework
Malaria is a potentially fatal plasmodium parasite injected by female
anopheles mosquitoes that infect red blood cells and millions worldwide yearly.
However, specialists' manual screening in clinical practice is laborious and
prone to error. Therefore, a novel Deep Boosted and Ensemble Learning (DBEL)
framework, comprising the stacking of new Boosted-BR-STM convolutional neural
networks (CNN) and the ensemble ML classifiers, is developed to screen malaria
parasite images. The proposed Boosted-BR-STM is based on a new
dilated-convolutional block-based split transform merge (STM) and feature-map
Squeezing-Boosting (SB) ideas. Moreover, the new STM block uses regional and
boundary operations to learn the malaria parasite's homogeneity, heterogeneity,
and boundary with patterns. Furthermore, the diverse boosted channels are
attained by employing Transfer Learning-based new feature-map SB in STM blocks
at the abstract, medium, and conclusion levels to learn minute intensity and
texture variation of the parasitic pattern. The proposed DBEL framework
implicates the stacking of prominent and diverse boosted channels and provides
the generated discriminative features of the developed Boosted-BR-STM to the
ensemble of ML classifiers. The proposed framework improves the discrimination
ability and generalization of ensemble learning. Moreover, the deep feature
spaces of the developed Boosted-BR-STM and customized CNNs are fed into ML
classifiers for comparative analysis. The proposed DBEL framework outperforms
the existing techniques on the NIH malaria dataset that are enhanced using
discrete wavelet transform to enrich feature space. The proposed DBEL framework
achieved Accuracy (98.50%), Sensitivity (0.9920), F-score (0.9850), and AUC
(0.997), which suggest it to be utilized for malaria parasite screening.Comment: 26 pages, 10 figures, 9 Table
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