Malaria detection using Deep Convolution Neural Network
Clicks: 80
ID: 283592
2023
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Emerging Content
23.7
/100
80 views
25 readers
AI Quality Assessment
Not analyzed
Readership in this journal
EmergingRanked #270 of 803 articles by views in arXiv
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 803 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
The latest WHO report showed that the number of malaria cases climbed to 219
million last year, two million higher than last year. The global efforts to
fight malaria have hit a plateau and the most significant underlying reason is
international funding has declined. Malaria, which is spread to people through
the bites of infected female mosquitoes, occurs in 91 countries but about 90%
of the cases and deaths are in sub-Saharan Africa. The disease killed 4,35,000
people last year, the majority of them children under five in Africa. AI-backed
technology has revolutionized malaria detection in some regions of Africa and
the future impact of such work can be revolutionary. The malaria Cell Image
Data-set is taken from the official NIH Website NIH data. The aim of the
collection of the dataset was to reduce the burden for microscopists in
resource-constrained regions and improve diagnostic accuracy using an AI-based
algorithm to detect and segment the red blood cells. The goal of this work is
to show that the state of the art accuracy can be obtained even by using 2
layer convolution network and show a new baseline in Malaria detection efforts
using AI.
| Reference Key |
kumar2023malaria
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Sumit Kumar; Harsh Vardhan; Sneha Priya; Ayush Kumar |
| Journal | arXiv |
| Year | 2023 |
| DOI |
DOI not found
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.