Multi-task advanced convolutional neural network for robust lymphoblastic leukemia diagnosis, classification, and segmentation.

ABSTRACT: Acute lymphoblastic leukemia (ALL), a hematologic malignancy characterized by the overproduction of immature lymphocytes, a type of white blood cell. Accurate and timely diagnosis of ALL is crucial for effective management. This article introduces a novel multi-task advanced convolutional neural network (MTA-CNN) framework for ALL detection in medical imaging data by simultaneously performing, expression classification, and disease detection. The MTA-CNN is based on a deep learning architecture that can handle multiple tasks simultaneously, allowing it to learn more comprehensive and generalizable features. With, expression classification, and disease detection tasks, the MTA-CNN effectively leverages the complementary information from each task to improve overall performance. The proposed

SUBMITTER: Yalcin S 

PROVIDER: S-EPMC12453769 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature