Automated Detection Model in Classification of B-Lymphoblast Cells from Normal B-Lymphoid Precursors in Blood Smear Microscopic Images Based on the Majority Voting Technique

Algorithm 1

The proposed ensemble algorithm.

	Data: lymphoblast images (X, Y), X is the number of dataset images, Y = {y ∈ }
Result: the proposed model based on the majority voting technique classifies the class of lymphocyte images xX
Applied preprocessing
Step 1: resizing images to dimensions 300 × 300
Step 2: dataset image normalization, where each image is mapped to [0, 255] and then converted to [0, 1] for xX.
Step 3: dataset edge detection and tuning brightness and contrast for xX
Step 4: data augmentation for every xX (rotation, flipping, cropping, and color transformation)
Implementing a set of pretrained Model M={DenseNet121, Inception V3, Inception-Reset-v2, ResNet-50, ResNet101-Xception, SqueezeNet, Alexnet}
Models K={DenseNet121, Inception V3, Inception-Reset-v2, ResNet-50, ResNet101-Xception, SqueezeNet, Alexnet} with the problem scale
for each, α = 0.0017, epochs = 700
for i = 1 to epochs
for each batch do
for calculating the output and updating the hyperparameter
if error is not improving, change hyperparameter
using dropout, validation, and then feed output of feature extraction block to Softmax
end
end
end
end
for each xxtest, do
majority-voting technique’s performance for all models
end