Detection of Acute Lymphoblastic Leukemia using SMMT and Watershed with DenseNet121 Classification
DOI:
https://doi.org/10.26905/jeemecs.v8i2.15545Keywords:
Acute Lymphoblastic Leukemia, Segmentation, Watershed, SMMT, DenseNet121Abstract
Acute Lymphoblastic Leukemia (ALL) is a type of blood cancer that commonly affects children and requires early detection to improve treatment success. This study proposes a method for ALL detection based on microscopic images of white blood cells using a deep learning approach. The research stages include data preprocessing with augmentation and the Self-Dual Multiscale Morphological Toggle (SMMT) method, segmentation using the Watershed method to separate the cell nucleus and cytoplasm, and classification using the DenseNet121 architecture. The dataset used consists of four classes of blood cells and is processed to be balanced before being trained on the model. The evaluation was carried out based on three batch-size testing scenarios to measure the effect of configuration on model performance. The results showed that the combination of SMMT and Watershed segmentation methods improved the quality of visual image features, while classification using DenseNet121 provided high accuracy with the best results at batch size 8, reaching 97%. This study proves that the combination of segmentation and deep learning techniques effectively detects ALL and can be further developed for automatic diagnostic systems.
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