Treffer: Improving real-time performance in Acute lymphoblastic leukemia classification using Mobilenet3 compared to densenet algorithm.

Improving the accuracy in Acute lymphoblastic leukemia classification is the major goal of this study.:The study made use of the kaggle dataset as the primary source of data. Two distinct groups, Group I and Group 2, each comprising 20 samples, were utilized in this study. Group I employed the MobileNet3, while Group 2 utilized the VGG16. The total sample size for the study was 40. Sample size calculations for statistical analysis, as well as the subsequent performance comparison were conducted and implementation was done using Python. The statistical analysis was carried out using clincalc.com with a statistical power (G-power) set at 85%, alpha (a) at 0.05, beta (13) at 0.2. The analysis primarily focused on comparing the performance of the MobileNet3 and Algorithm using accuracy value as the key evaluation metric. In terms of accuracy, MobileNet3 (95.74%) outperforms VGG16 (81.773%), with a two-tailed, p>0.05 significance value of <.001. In summary, the accuracy of MobileNet3 outperforms VGG1 accuracy. [ABSTRACT FROM AUTHOR]