Classification of Acute Lymphoblastic Leukemia based on White Blood Cell Images using InceptionV3 Model

Classification of Acute Lymphoblastic Leukemia based on White Blood Cell Images using InceptionV3 Model

ALL; CNN; deep learning; inceptionV3; leukemia

Acute Lymphoblastic Leukemia (ALL) is the most prevalent form of leukemia that occurs in children. Detection of ALL through
white blood cell image analysis can assist in prognosis and appropriate treatment. In this study, the author proposes an
approach for classifying ALL based on white blood cell images using a Convolutional Neural Network (CNN) model called
InceptionV3. The dataset used in this research consists of white blood cell images collected from patients with ALL and healthy
individuals. These images were obtained from The Cancer Imaging Archive (TCIA), which is a service for storing large-scale
cancer medical images available to the public. During the evaluation phase, the author used training data evaluation metrics
such as accuracy and loss to measure the model's performance. The research results show that the InceptionV3 model is
capable of classifying white blood cell images with a high level of accuracy. This model achieves an average ALL recognition
accuracy of 0.9896 with a loss of 0.031. The use of CNN models like InceptionV3 in medical image analysis has the potential
to enhance the efficiency and accuracy of image-based disease diagnosis

Rizki Firdaus Mulya, Ema Utami, Dhani Ariatmanto

http://jurnal.iaii.or.id

Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association

August 2023

Sri Wahyuni

ISSN Media Electronic: 2580-0760

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