Automating Leukemia Diagnosis with Autoencoders: A Comparative Study
This work addresses the problem of improving diagnostic accuracy for leukemia patients, but it is incremental as it focuses on optimizing autoencoder parameters rather than introducing a new paradigm.
The paper tackled leukemia diagnosis by using autoencoders to extract features from medical data, achieving over 11% improvement in precision and F1-score compared to classical machine learning models.
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%.