Automating Leukemia Diagnosis with Autoencoders: A Comparative Study
Minoo Sayyadpour Nasibe Moghaddamniya Touraj Banirostam
Leukemia is one of the most common and death-threatening types of cancer that
threaten human life. Medical data from some of the patient's critical
parameters contain valuable information hidden among these data. On this
subject, deep learning can be used to extract this information. In this paper,
AutoEncoders have been used to develop valuable features to help the precision
of leukemia diagnosis. It has been attempted to get the best activation
function and optimizer to use in AutoEncoder and designed the best architecture
for this neural network. The proposed architecture is compared with this area's
classical machine learning models. Our proposed method performs better than
other machine learning in precision and f1-score metrics by more than 11%.