Fig 1.
Illustration of how data augmentation algorithm works. A, 25 intact random images from the dataset; B, C, and D, random data augmentations applied to that same 25 images, which would then feed to the model.
Fig 2.
The 340 layers of the EfficientNET-B1 is collapsed.
Fig 3.
The alterations in loss and accuracy of the model’s predictions per epoch is illustrated.
Fig 4.
Dataset description.
Fig 5.
Frequency of each type of skin cancer lesions in the dataset.
Fig 6.
ANOVA test to compare mean age between different classes of skin cancer and gender.
Dashed lines indicate Mean and ± one standard deviation interval from Mean. Dots indicate outliners.
Table 1.
Comparing the mean age between skin cancer lesions.
Table 2.
The model alterations description per epoch during training.
Fig 7.
AKIEC: Actinic Keratosis; BCC: Basal Cell Carcinoma; BKL: Benign Keratosis; DF: Dermatofibroma; MEL: Melanoma; NV: Melanocytic Nevi; VASC: Vascular Lesions.
Fig 8.
The top 20 most wrong predictions.
Actual: the true label of each image; pred: the model’s predicted class; prob: the probability inferred to the predicted class by the model.