DSCNet: Deep Skip Connections-Based Dense Network for ALL Diagnosis Using Peripheral Blood Smear Images.

ABSTRACT: Acute lymphoblastic leukemia (ALL) is a life-threatening hematological malignancy that requires early and accurate diagnosis for effective treatment. However, the manual diagnosis of ALL is time-consuming and can delay critical treatment decisions. To address this challenge, researchers have turned to advanced technologies such as deep learning (DL) models. These models leverage the power of artificial intelligence to analyze complex patterns and features in medical images and data, enabling faster and more accurate diagnosis of ALL. However, the existing DL-based ALL diagnosis suffers from various challenges, such as computational complexity, sensitivity to hyperparameters, and difficulties with noisy or low-quality input images. To address these issues, in this paper, we propose a novel

SUBMITTER: Kaur M 

PROVIDER: S-EPMC10486457 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature