Acute lymphocytic leukemia detection using hybrid deep learning models
Author(s): Louai Zaiter
Abstract: Acute Lymphocytic Leukemia is a type of cancer that affects white blood cells and it spreads quickly.This study proposes a computer-aided diagnosis system to detect this type of leukemia from blood microscopic images. We introduce a hybrid machine learning model that uses a ResNet18 encoder to extract latent embeddings from the multi-otsu segmented white blood cells and we feed those embeddings into machine learning classifiers. The random forest and the k-nearest neighbours recorded the best classification accuracy i.e. 98% while misclassifying two samples from the ALL-IDB dataset.
Louai Zaiter. Acute lymphocytic leukemia detection using hybrid deep learning models. Int J Eng Comput Sci 2025;7(1):142-144. DOI: 10.33545/26633582.2025.v7.i1b.170
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