Multi-model ensemble to classify acute lymphoblastic leukemia in blood smear images
Acute Lymphoblastic Leukemia (ALL) is one of the most commonly occurring type of leukemia which poses a serious threat to life. It severely affects White Blood Cells (WBCs) of the human body that fight against any kind of infection or disease. Since, there are no evident morphological changes and the signs are pretty similar to other disorders, it becomes difficult to detect leukemia. Manual diagnosis of leukemia is time-consuming and is even susceptible to errors. Thus, in this paper, computer assisted diagnosis method has been implemented to detect leukemia using deep learning models. Three models namely, VGG11, ResNet18 and ShufflenetV2 have been trained and fine tuned on ISBI 2019 C-NMC dataset. Finally an ensemble using weighted averaging technique is formed and evaluated as per the criteria of binary classification. The proposed method gave an overall accuracy of 87.52% and F1-score of 87.40%. Thus, it outperforms most of the existing techniques for the same dataset.
-
Category
No comments found