Malaria Screener: a smartphone application for automated malaria screening.

BACKGROUND: Light microscopy is often used for malaria diagnosis in the field. However, it is time-consuming and quality of the results depends heavily on the skill of microscopists. Automating malaria light microscopy is a promising solution, but it still remains a challenge and an active area of research. Current tools are often expensive and involve sophisticated hardware components, which makes it hard to deploy them in resource-limited areas. RESULTS: We designed an Android mobile application called Malaria Screener, which makes smartphones an affordable yet effective solution for automated malaria light microscopy. The mobile app utilizes high-resolution cameras and computing power of modern smartphones to screen both thin and thick blood smear images for P. falciparum parasites. Malaria Screener combines image acquisition, smear image analysis, and result visualization in its slide screening process, and is equipped with a database to provide easy access to the acquired data. CONCLUSION: Malaria Screener makes the screening process faster, more consistent, and less dependent on human expertise. The app is modular, allowing other research groups to integrate their methods and models for image processing and machine learning, while acquiring and analyzing their data.

Detection of live breast cancer cells in bright-field microscopy images containing white blood cells by image analysis and deep learning.

SIGNIFICANCE: Circulating tumor cells (CTCs) are important biomarkers for cancer management. Isolated CTCs from blood are stained to detect and enumerate CTCs. However, the staining process is laborious and moreover makes CTCs unsuitable for drug testing and molecular characterization. AIM: The goal is to develop and test deep learning (DL) approaches to detect unstained breast cancer cells in bright-field microscopy images that contain white blood cells (WBCs). APPROACH: We tested two convolutional neural network (CNN) approaches. The first approach allows investigation of the prominent features extracted by CNN to discriminate in vitro cancer cells from WBCs. The second approach is based on faster region-based convolutional neural network (Faster R-CNN). RESULTS: Both approaches detected cancer cells with higher than 95% sensitivity and 99% specificity with the Faster R-CNN being more efficient and suitable for deployment presenting an improvement of 4% in sensitivity. The distinctive feature that CNN uses for discrimination is cell size, however, in the absence of size difference, the CNN was found to be capable of learning other features. The Faster R-CNN was found to be robust with respect to intensity and contrast image transformations. CONCLUSIONS: CNN-based DL approaches could be potentially applied to detect patient-derived CTCs from images of blood samples.