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RK Rana Khaled
MH Maha Helal
OA Omar Alfarghaly
OM Omnia Mokhtar
AE Abeer Elkorany
HK Hebatalla El Kassas
AF Aly Fahmy
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An EfficientNetB017, pre-trained on ImageNet18, was used as the starting model in our experiments. We finetuned the model by removing the final layer and adding a layer with three output classes (Normal, Benign, Malignant). All the weights are left to be fine-tuned during the training. Categorical cross-entropy was used as the loss function with Adam optimizer19 as shown in Eq. 1, where CE(b) is the cross entropy loss for batch b, C the number of classes, N the number of images in the batch, y is the ground-truth, and y^ is the prediction. A batch size of 16 was used, a decaying learning rate of 1e-3, and a dropout layer20 with a drop probability of 0.8 on the final visual features was used before the classifier.

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