iMAGING: a novel automated system for malaria diagnosis by using artificial intelligence tools and a universal low-cost robotized microscope

Artificial intelligence; Malaria diagnosis; Robotized microscopeInteligencia artificial; Diagnóstico de malaria; Microscopio robotizadoIntel·ligència artificial; Diagnòstic de malària; Microscopi robotitzatIntroduction: Malaria is one of the most prevalent infectious diseases in sub-Saharan Africa, with 247 million cases reported worldwide in 2021 according to the World Health Organization. Optical microscopy remains the gold standard technique for malaria diagnosis, however, it requires expertise, is time-consuming and difficult to reproduce. Therefore, new diagnostic techniques based on digital image analysis using artificial intelligence tools can improve diagnosis and help automate it. Methods: In this study, a dataset of 2571 labeled thick blood smear images were created. YOLOv5x, Faster R-CNN, SSD, and RetinaNet object detection neural networks were trained on the same dataset to evaluate their performance in Plasmodium parasite detection. Attention modules were applied and compared with YOLOv5x results. To automate the entire diagnostic process, a prototype of 3D-printed pieces was designed for the robotization of conventional optical microscopy, capable of auto-focusing the sample and tracking the entire slide. Results: Comparative analysis yielded a performance for YOLOv5x on a test set of 92.10% precision, 93.50% recall, 92.79% F-score, and 94.40% mAP0.5 for leukocyte, early and mature Plasmodium trophozoites overall detection. F-score values of each category were 99.0% for leukocytes, 88.6% for early trophozoites and 87.3% for mature trophozoites detection. Attention modules performance show non-significant statistical differences when compared to YOLOv5x original trained model. The predictive models were integrated into a smartphone-computer application for the purpose of image-based diagnostics in the laboratory. The system can perform a fully automated diagnosis by the auto-focus and X-Y movements of the robotized microscope, the CNN models trained for digital image analysis, and the smartphone device. The new prototype would determine whether a Giemsa-stained thick blood smear sample is positive/negative for Plasmodium infection and its parasite levels. The whole system was integrated into the iMAGING smartphone application. Conclusion: The coalescence of the fully-automated system via auto-focus and slide movements and the autonomous detection of Plasmodium parasites in digital images with a smartphone software and AI algorithms confers the prototype the optimal features to join the global effort against malaria, neglected tropical diseases and other infectious diseases.The project is funded by the Microbiology Department of Vall d’Hebron University Hospital, the Cooperation Centre of the Universitat Politècnica de Catalunya (CCD-UPC), and the Probitas Foundation

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

Malarial Diagnosis with Deep Learning and Image Processing Approaches

Malaria is a mosquito-borne disease that has killed an estimated a half-a-million people worldwide since 2000. It may be time consuming and costly to conduct thorough laboratory testing for malaria, and it also requires the skills of trained laboratory personnel. Additionally, human analysis might make mistakes. Integrating denoising and image segmentation techniques with Generative Adversarial Network (GAN) as a data augmentation technique can enhance the performance of diagnosis. Various deep learning models, such as CNN, ResNet50, and VGG19, for recognising the Plasmodium parasite in thick blood smear images have been used. The experimental results indicate that the VGG19 model performed best by achieving 98.46% compared to other approaches. This study demonstrates the potential of artificial intelligence to improve the speed and precision of pathogen detection which is more effective than manual analysis

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

CNN Classification of Malaria Parasites in Digital Microscope Images Using Python on Raspberry Pi

Malaria disease occurs because the plasmodium parasite infects human red blood cells spread by female Anopheles mosquitoes and then causes health problems such as blood deficiency and even death. The gold standard of malaria diagnosis is to use laboratory microscopy examination of the patient's red blood cell samples to distinguish between microscope images of parasitic and non-parasitic red blood cells. However, diagnosing malaria through microscope observation is time-consuming, subjective, and tiring for health workers. So, a malaria classification system was designed using the Convolutional Neural Network (CNN) method to distinguish parasitic and non-parasitic red blood cell images. The CNN model is trained using training data and also tested using test data. Then, the CNN training model is embedded on a Raspberry Pi equipped with a Graphical User Interface to facilitate observer interaction through the LCD screen on this digital and portable microscope. The CNN classification rate achieved an accuracy value of 97.88% using the database image and 98.76% using the digital microscope acquisition image. The CNN classification system of malaria parasites designed on a Raspberry Pi-based digital and portable microscope is expected to improve the diagnosis of malaria and reduce the infection rate of malaria patients, especially in various remote areas in Indonesia

A Novel Data Augmentation Convolutional Neural Network for Detecting Malaria Parasite in Blood Smear Images

Malaria fever is a potentially fatal disease caused by the Plasmodium parasite. Identifying Plasmodium parasites in blood smear images can help diagnose malaria fever rapidly and precisely. According to the World Health Organization (WHO), there were 241 million malaria cases and 627 000 deaths worldwide in 2020, while 95% of malaria cases and 96% of malaria deaths occurred in Africa. Also in Africa, children that are less than five years old accounted for an estimated 80% of all malaria deaths. To address the menace of malaria, this paper proposes a novel deep learning model, called a data augmentation convolutional neural network (DACNN), trained by reinforcement learning to tackle this problem. The performance of the proposed DACNN model is compared with CNN and directed acyclic graph convolutional neural network (DAGCNN) models. Results show that DACNN outperforms previous studies in processing and classification images. It achieved 94.79% classification accuracy in malaria blood sample images of balanced class dataset obtained from the Kaggle dataset. The proposed model can serve as an effective tool for the detection of malaria parasites in blood smear images.publishedVersio