A Systematic Review of Acute Leukemia Diagnosis by Using Deep Learning

Clicks: 1
ID: 313220
2023
Free PDF
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.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #628 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 minted

Create 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
Sign in to mint
Abstract
Acute leukaemia, a malignancy that starts in the bone marrow, manifests as an unchecked, fast spread of white blood cells. Acute lymphoblastic leukaemia is more common in young kids under the age of 5 and older people over the age of 50. Automated diagnosis systems are increasingly using image analysis and Deep Learning techniques due to their great accuracy in a variety of health diagnostic sectors, such as the detection of Acute Lymphoblastic Leukemia from serum samples. The several automated techniques for identifying and categorising acute leukaemia are discussed in this systematic review research along with the challenges the authors faced. In this review, Sahlol, A.T. et al. employed ALL-IDB2 to performed segmentation using the Zack algorithm and achieved the highest segmentation accuracy (99.23%), sensitivity (100%) and uniqueness (97.1%). The study makes use of a number of methodologies, including thresholding, the Zack algorithm, the clustering-based technique, the fuzzy C-mean, the k-myeloid, and the k-means. In the domain of medical image analysis, deep learning worked remarkably well, and CNN made it extremely straightforward to build an end-to-end network. The numerous steps leading up to classification, including preprocessing, augmentation, segmentation, and feature extraction—all of which are discussed in this study.
Reference Key
imported_1777059858_69ebc81254375 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Anum Yousaf
Journal Journal of Computing & Biomedical Informatics
Year 2023
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/113
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.