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

Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review

Deep learning; Malaria diagnosis; Microscopic examinationAprenentatge profund; Diagnòstic de malària; Examen microscòpicAprendizaje profundo; Diagnóstico de malaria; Examen microscópicoMalaria 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.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 Foundation

Reducing data dimension boosts neural network-based stage-specific malaria detection

Although malaria has been known for more than 4 thousand years(1), it still imposes a global burden with approx. 240 million annual cases(2). Improvement in diagnostic techniques is a prerequisite for its global elimination. Despite its main limitations, being time-consuming and subjective, light microscopy on Giemsa-stained blood smears is still the gold-standard diagnostic method used worldwide. Autonomous computer assisted recognition of malaria infected red blood cells (RBCs) using neural networks (NNs) has the potential to overcome these deficiencies, if a fast, high-accuracy detection can be achieved using low computational power and limited sets of microscopy images for training the NN. Here, we report on a novel NN-based scheme that is capable of the high-speed classification of RBCs into four categories—healthy ones and three classes of infected ones according to the parasite age—with an accuracy as high as 98%. Importantly, we observe that a smart reduction of data dimension, using characteristic one-dimensional cross-sections of the RBC images, not only speeds up the classification but also significantly improves its performance with respect to the usual two-dimensional NN schemes. Via comparative studies on RBC images recorded by two additional techniques, fluorescence and atomic force microscopy, we demonstrate that our method is universally applicable for different types of microscopy images. This robustness against imaging platform-specific features is crucial for diagnostic applications. Our approach for the reduction of data dimension could be straightforwardly generalised for the classification of different parasites, cells and other types of objects

Fully-automated patient-level malaria assessment on field-prepared thin blood film microscopy images, including Supplementary Information

Malaria is a life-threatening disease affecting millions. Microscopy-based assessment of thin blood films is a standard method to (i) determine malaria species and (ii) quantitate high-parasitemia infections. Full automation of malaria microscopy by machine learning (ML) is a challenging task because field-prepared slides vary widely in quality and presentation, and artifacts often heavily outnumber relatively rare parasites. In this work, we describe a complete, fully-automated framework for thin film malaria analysis that applies ML methods, including convolutional neural nets (CNNs), trained on a large and diverse dataset of field-prepared thin blood films. Quantitation and species identification results are close to sufficiently accurate for the concrete needs of drug resistance monitoring and clinical use-cases on field-prepared samples. We focus our methods and our performance metrics on the field use-case requirements. We discuss key issues and important metrics for the application of ML methods to malaria microscopy.Comment: 16 pages, 13 figure

Deep learning networks with adaptive attention mechanisms for malaria detection in thick smear blood samples

This project aims to develop an end-to end deep network to detect malaria parasites from thick smear blood sample images with object detectors. This project uses different objects detectors such as Faster-RCNN, SSD, RetinaNet and YOLO. The model with the best results is YOLO, so adaptive attention mechanisms are implemented in this model, such as Squeeze-and-Excitation (SE) and Convolution Block Attention Module (CBAM).Este proyecto tiene como objetivo desarrollar una red profunda de extremo a extremo para detectar parásitos de la malaria a partir de imágenes de muestras de sangre de frotis gruesos con detectores de objetos. Este proyecto utiliza diferentes detectores de objetos como Faster-RCNN, SSD, RetinaNet y YOLO. El modelo con mejores resultados es YOLO, por lo que en este modelo se implementan mecanismos de atención adaptativa, como Squeeze-and-Excitation (SE) y Convolution Block Attention Module (CBAM).Aquest projecte pretén desenvolupar una xarxa profunda d'extrem a extrem per detectar paràsits de la malària a partir d'imatges de mostres de sang de frotis gruixut amb detectors d'objectes. Aquest projecte utilitza diferents detectors d'objectes com Faster-RCNN, SSD, RetinaNet i YOLO. El model amb millors resultats és YOLO, per la qual cosa s'implementen mecanismes d'atenció adaptativa en aquest model, com el Squeeze-and-Excitation (SE) i el Convolution Block Attention Module (CBAM)

Classification and quantification of malaria parasites using convolutional neural networks.

Applied Thesis submitted to the Department of Computer Science, Ashesi University, in partial fulfillment of Bachelor of Science degree in Computer Science, April 2018.Malaria is currently one of the most deadly diseases in the world. While there are different treatment methods for the disease, the search for new drugs against malaria is still a very important area of research. One of the main challenges in manufacturing drugs against malaria is efficiently evaluating the performance of the drugs on the parasites since it requires, amongst others, precise measurements of the parasite growth-stages as well as their counts in blood smear images. The current gold-standard for making such detail diagnosis is manual microscopy which is tedious. This research showed that convolutional neural networks can be used to identify the different growth-cycle stages of Plasmodium parasites, even in situations where there is little data. Employing a variety of data augmentation techniques and transfer learning, a semantic segmentation model was built to discriminate between trophozoites, gametocytes and normal red blood cells with an accuracy of 85.86% in 353 Giemsa-stained thin blood smears. The results showed that it is possible to perform densepredictionsonGiemsa-stained thin blood smears using convolutional neural networks

Classification and quantification of malaria parasites using convolutional neural networks

Undergraduate thesis submitted to the Department of Computer Science, Ashesi University, in partial fulfillment of Bachelor of Science degree in Computer Science, April 2018Malaria is currently one of the most deadly diseases in the world. While there are different treatment methods for the disease, the search for new drugs against malaria is still a very important area of research. One of the main challenges in manufacturing drugs against malaria is efficiently evaluating the performance of the drugs on the parasites since it requires, amongst others, precise measurements of the parasite growth-stages as well as their counts in blood smear images. The current gold-standard for making such detail diagnosis is manual microscopy which is tedious. This research showed that convolutional neural networks can be used to identify the different growth-cycle stages of Plasmodium parasites, even in situations where there is little data. Employing a variety of data augmentation techniques and transfer learning, a semantic segmentation model was built to discriminate between trophozoites, gametocytes and normal red blood cells with an accuracy of 85.86% in 353 Giemsa-stained thin blood smears. The results showed that it is possible to perform dense predictions on Giemsa-stained thin blood smears using convolutional neural networks.Ashesi Universit

A deep architecture based on attention mechanisms for effective end-to-end detection of early and mature malaria parasites

Malaria is a severe infectious disease caused by the Plasmodium parasite. The early and accurate detection of this disease is crucial to reducing the number of deaths it causes. However, the current method of detecting malaria parasites involves manual examination of blood smears, which is a time-consuming and labor-intensive process, mainly performed by skilled hematologists, especially in underdeveloped countries. To address this problem, we have developed two deep learning-based systems, YOLO-SPAM and YOLO-SPAM++, which can detect the parasites responsible for malaria at an early stage. Our evaluation of these systems using two public datasets of malaria parasite images, MP-IDB and IML, shows that they outperform the current state-of-the-art, with more than 11M fewer parameters than the baseline YOLOv5m6. YOLO-SPAM++ demonstrated a substantial 10% improvement over YOLO-SPAM and up to 20% against the best-performing baseline in preliminary experiments conducted on the Plasmodium Falciparum species of MP-IDB. On the other hand, YOLO-SPAM showed slightly better results than YOLO-SPAM++ in subsets without tiny parasites, while YOLO-SPAM++ performed better in subsets with tiny parasites, with precision values up to 94%. Further cross-species generalization validations, conducted by merging training sets of various species within MP-IDB, showed that YOLO-SPAM++ consistently outperformed YOLOv5 and YOLO-SPAM across all species, emphasizing its superior performance and precision in detecting tiny parasites. These architectures can be integrated into computer-aided diagnosis systems to create more reliable and robust systems for the early detection of malaria