Comparative analysis of random forest and deep learning approaches for automated acute lymphoblastic leukemia detection using morphologicaland textural features

Comparative analysis of random forest and deep learning approaches for automated acute lymphoblastic leukemia detection using morphologicaland textural features

Acute Lymphoblastic Leukemia (ALL) is a type of blood cancer that requires early and accurate detection for effective treatment. Current diagnostic approaches face significant challenges including time-consuming manual examination, inter-observervariability, and difficulty in balancing sensitivity...

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Bibliographic Details
Main Authors: Windra Swastika, Kestrilia Rega Prilianti, Paulus Lucky Tirma Irawan, Hendry Setiawan
Format: Article
Language:English
Published: Informatics Department, Engineering Faculty 2025-07-01
Series:Jurnal Ilmiah Kursor: Menuju Solusi Teknologi Informasi
Subjects:
acute lymphoblastic leukemia
Deep Learning
medical image analysis
morphological features
Random Forest
Online Access:https://kursorjournal.org/index.php/kursor/article/view/427
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