Hematologic Cancer Detection Using White Blood Cancerous Cells Empowered with Transfer Learning and Image Processing
Table 2
Pseudocode of the proposed model (this depicts the algorithm of the current study and research methodology from data fetching to training and testing and also it explains every training model).
Steps
Codes
1
Input cancerous white blood cell images
2
Image preprocessing
3
Data division into training and testing
4
Store into cloud (£)
5
Input training images to deep learning algorithms
6
AlexNet
1- SGDM
2- Adaptive moment (ADAM)
3- Root mean square propagation (RMSPROP)
Check learning criteria
If meet
Store into private cloud
Else applying image preprocessing
Input image preprocessed images to deep learning algorithm
AlexNet
4- SGDM
5- ADAM
6- RMSPROP
Check learning criteria
If meet
Store into private cloud
Else
Retrain
7
ResNet
1- SGDM
2- ADAM
3- RMSPROP
Check learning criteria
If meet
Store into private cloud
Else applying image preprocessing
Input image preprocessed images to deep learning algorithm
ResNet
4- SGDM
5- ADAM
6- RMSPROP
Check learning criteria
If meet
Store into private cloud
Else
Retrain
8
ResNet
1- SGDM
2- ADAM
3- RMSPROP
Check learning criteria
If meet
Store into private cloud
Else applying image preprocessing
Input image preprocessed images to deep learning algorithm
ResNet
4- SGDM
5- ADAM
6- RMSPROP
Check learning criteria
If meet
Store into private cloud
Else
Retrain
9
Access all private cloud
Check the learning criteria of the best deep learning trained models
If meet
Select semi-best model and store it in other private cloud
