Utilizing Image Analysis with Machine Learning and Deep Learning to Identify Malaria Parasites in Conventional Microscopic Blood Smear Images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Malaria, a mosquito-borne blood infection, is caused by the Plasmodium genus.The traditional diagnostic approach relies on the manual examination of stained blood cells under a microscope.However, this labour-intensive and time-consuming process can be significantly improved with machine learning techniques to analyze microscopic images of blood smears for parasite detection.This paper reviews the various methodologies previously employed, focusing on the different strategies used for imaging.A comprehensive summary is provided, detailing the work conducted on both thin and thick blood smear images.Emerging developments in deep learning, coupled with contemporary mobile technologies, are also highlighted as potential future tools for malaria diagnosis.The paper further explores recent advancements in machine learning techniques for malaria detection and identification in images, emphasizing challenges associated with image processing.In addition, a detailed comparison is made between various machine learning approaches to provide a comprehensive overview.The application of these advanced machine learning and deep learning techniques holds the potential to revolutionize the process of malaria detection and control.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it