A Deep Learning Framework for Leukemia Cancer Detection in Microscopic Blood Samples Using Squeeze and Excitation Learning

Table 7

Comparison with existing work.

Sr. No.	Authors	Methods	Dataset	Accuracy (%)
1	Ahmed et al. [32]	CNN	ALL_IDB	88.25
2	Shafique and Tehsin [2]	AlexNet-based transfer learning	ALL_IDB	99.50
3	Jothi et al. [74]	Jaya, SVM	ALL_IDB	99
4	Mishra et al. [42]	DOST, PCA, LDA	ALL_IDB1	99.66
5	Jiang et al. [54]	Vision transformer– based CNN	ISBI2019	99.03
6	Agaian et al. [75]	SVM with cell energy feature	ALL_IDB1	94
7	Tuba and Tuba [60]	Gao-based methods	ALL_IDB2	93.84
8	Jha and Dutta [58]	SCA-based deep CNN	ALL_IDB2	98.70
9	Proposed	Squeeze and excitation based CNN	ALL_IDB1	100
10	Proposed	Squeeze and excitation based CNN	ALL_IDB2	99.98
11	Proposed	Squeeze and excitation based CNN	ALL_IDB1 + ALL_IB2	98.3