Fig 2 - available via license: Creative Commons Zero 1.0
Content may be subject to copyright.
Source publication
Thousands of individuals succumb annually to leukemia alone. This study explores the application of image processing and deep learning techniques for detecting Acute Lymphoblastic Leukemia (ALL), a severe form of blood cancer responsible for numerous annual fatalities. As artificial intelligence technologies advance, the research investigates the r...
Contexts in source publication
Context 1
... accuracy graph for the nano version of YOLOv11 shown in Fig. 2b demonstrates improvement in the accuracy's progress with some fluctuations at the beginning. These variations decrease gradually as the number of epochs increases until the graph curve becomes more stable. It can be seen that the training and validation losses were declining steadily as the training advanced in Figures. 2a and 6a. The ...
Context 2
... version of YOLOv11 shown in Fig. 2b demonstrates improvement in the accuracy's progress with some fluctuations at the beginning. These variations decrease gradually as the number of epochs increases until the graph curve becomes more stable. It can be seen that the training and validation losses were declining steadily as the training advanced in Figures. 2a and 6a. The confusion matrix in Fig. 3 offers valuable insights into the YOLOv11s model's performance, highlighting which classes are accurately detected and where errors occur. This analysis helps identify areas for improvement to enhance the model's effectiveness. The matrix indicates that the model achieved high accuracy in detecting ...
Similar publications
Thousands of individuals succumb annually to leukemia alone. As artificial intelligence-driven technologies continue to evolve and advance, the question of their applicability and reliability remains unresolved. This study aims to utilize image processing and deep learning methodologies to achieve state-of-the-art results for the detection of Acute...
