Harnessing Artificial Intelligence for Lung and Colon Cancer Classification via CNN
Clicks: 1
ID: 312886
2024
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
0.0
/100
1 views
0 readers
AI Quality Assessment
Not analyzed
Readership in this journal
Ranked #446 of 705 articles by views in Journal of Computing & Biomedical Informatics
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 705 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 aggressiveness, strong propensity to spread, and heterogeneity of cancer seem to be the main causes of its very high fatality rate. Throughout the world, lung and colon cancers are two of the most common malignancies that affect individuals of all ages. Accurate and timely detection of these cancers may improve the best aspects of treatment and increase the survival rate. As a complement to the current cancer detection techniques, an extremely exact and computationally effective model is proposed for the rapid and accurate diagnosis of cancers in the lung and colon area. By employing a cyclic learning rate, the accuracy of the proposed techniques is increased while maintaining their processing efficiency. This is easy to use and effective, which speeds up the model's convergence. Furthermore, many transfer learning models that have already been trained are used and compared with the proposed CNN that has attention layers. The validation, testing, and training of the study make use of the LC25000 dataset. It is observed that the proposed model reduces the impact of inter-class disparities between lung adenocarcinoma and lung cancer of squamous cells by offering higher accuracy. By putting the proposed framework into practice, accuracy was improved to 99.04%.
| Reference Key |
imported_1777057607_69ebbf47a3a11
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Zulqarnain Iqbal, Adnan Ahmed Rafique, Areeba Sarwar, Maryam Izat |
| Journal | Journal of Computing & Biomedical Informatics |
| Year | 2024 |
| DOI |
DOI not found
|
| URL | |
| Keywords | Keywords not found |
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.