Eganoosi Atojunere, Temilola. Adewunmi Onaneye

Detecting Malaria Cells with Plasmo-D Expert System Developed on Android and Computer Vision

Introduction

Detecting malaria cells with plasmo-d expert system developed on android and computer vision. Plasmo-D is an Android expert system using computer vision to detect malaria-infected cells. Achieve 99.8% accuracy in differentiating infected/uninfected cells for faster diagnosis.

Abstract

Separation between infected and uninfected cells during diagnosing malaria parasites plasmodium is difficult, time-consuming, and expensive. However, this article presented a report on a developed expert system called Plasmodium Detector(Plasmo-D), capable of differentiating plasmodium-infected and uninfected cells from malaria-infected patients. Plasmo-D was built on an Android application, with an information menu, splash, and classification screen, including an image recognition system that worked with computer vision.  27,528 cell images were collected online from the Data Library of the United States National Library of Medicine, containing infected and uninfected cells for training. No cell images were used as control. Plasmo-D fabrication and testing were conducted at the Instrumentation Laboratory, Department of Systems Engineering, University of Lagos, Nigeria. Studied parameters included cell images, backgrounds, visual style, size, type, lighting, and camera angle. Trained models were exported into an Android application through Java programming language and user interface through Android XML (Extensible Markup Language). Trained data results indicated that 99.8% desired level of accuracy was obtained after cell images were fed into the computer vision application programming interface. The trend was that Plasmo-D efficiency was higher for infected image cells, average for uninfected image cells and the least for no cell photo.

Review

The article presents Plasmo-D, an Android-based expert system utilizing computer vision for the automatic detection of *Plasmodium*-infected and uninfected cells. This addresses a critical need in malaria diagnosis, which is currently labor-intensive, time-consuming, and expensive. A significant strength lies in the reported high accuracy of 99.8% obtained after training on a substantial dataset of 27,528 cell images sourced from the United States National Library of Medicine. The system's deployment on an Android platform enhances its potential for accessibility and use in resource-limited settings, making it a promising tool for improving diagnostic efficiency by leveraging widespread mobile technology. While the reported accuracy is impressive, several aspects warrant further detail and scrutiny. The abstract explicitly states that "No cell images were used as control," which is a notable omission in evaluating the system's robustness and generalizability, particularly against potential confounding factors or varied sample qualities. Furthermore, the abstract lacks specifics regarding the underlying computer vision model architecture (e.g., type of neural network, transfer learning approach), which is crucial for understanding the methodology and reproducibility. While parameters like lighting and camera angle were reportedly studied, the reliance solely on an online dataset without a concurrent real-world control or validation raises questions about its performance in diverse clinical conditions. The statement that "Plasmo-D efficiency was higher for infected image cells, average for uninfected image cells and the least for no cell photo" is vague and indicates a need for more granular performance metrics beyond overall accuracy, such as sensitivity, specificity, precision, recall, and false positive/negative rates, particularly concerning the 'no cell photo' category. Despite these points, the development of Plasmo-D represents a significant conceptual step towards accessible and efficient malaria diagnosis, particularly given its reported high accuracy and mobile application. For a full manuscript, it would be essential to elaborate on the exact methodology for dataset splitting (training/validation/test sets), provide a thorough analysis of misclassifications, and detail the choice of computer vision model. Crucially, future work should include a robust validation strategy incorporating a prospective clinical dataset, ideally with a clear control group and comparison against established diagnostic methods (both manual microscopy and other automated solutions), to truly assess its performance and clinical utility. This work lays a strong foundation but requires more rigorous validation and methodological transparency to achieve its full potential and warrant widespread adoption.

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