Name
Abstract | ||
|---|---|---|
Acute Lymphoblastic Leukemia (ALL) is a life-threatening type of cancer wherein mortality rate is unquestionably high. Early detection of ALL can reduce both the rate of fatality as well as improve the diagnosis plan for patients. In this study, we developed the ALL Detector (ALLD), which is a deep learning-based network to distinguish ALL patients from healthy individuals based on blast cell microscopic images. We evaluated multiple DL-based models and the ResNet-based model performed the best with 98% accuracy in the classification task. We also compared the performance of ALLD against state-of-the-art tools utilized for the same purpose, and ALLD outperformed them all. We believe that ALLD will support pathologists to explicitly diagnose ALL in the early stages and reduce the burden on clinical practice overall. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3233/SHTI210863 | ICIMTH |
Keywords | DocType | Volume |
Acute lymphoblastic leukemia,Computer aided diagnosis (CAD),Deep learning,Leukemia | Conference | 289 |
ISSN | Citations | PageRank |
1879-8365 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Saleh Musleh | 1 | 0 | 0.34 |
| Mohammad Tariqul Islam | 2 | 0 | 2.03 |
| Mohammad Towfik Alam | 3 | 0 | 0.34 |
| Mowafa Househ | 4 | 0 | 2.37 |
| Zubair Shah | 5 | 0 | 2.70 |
| Tanvir Alam | 6 | 0 | 2.70 |