KLASIFIKASI PENYAKIT LEUKEMIA PADA ANAK MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN) DENGAN ARSITEKTUR VGG-19 DAN INCEPTION-V4

Acute lymphoblastic leukaemia (ALL), also known as lymphocytic leukaemia, is a type of cancer that continues to develop in children as well as adults, Early diagnosis of ALL is crucial for effective treatment. In this study, the authors propose a deep learning approach using Convolutional Neural Network (CNN) with VGG-19 and Inception-V4 architecture for ALL cell image classification. In this study, each architecture is tested using several different schemes to assess the performance of the model in classifying Leukemia disease. The VGG-19 Batch Normalisation scheme shows the best performance with accuracy in training data reaching 85.43%, in validation 83.67% and in testing 83.56% and lower loss values of 0.3430, in training data, 0.3903, in validation, and 0.4101% in testing.