Abstract:
The frequency and fatality rates associated with skin
Melanoma requires an accurate and efficient detection
methodology to enable early medical diagnosis. Artificial Intelligence (AI) augmented detection methods aim at
achieving this goal while reducing the costs and time involved in traditional methods. This work utilizes a two-level
ensemble learning technique (trained with weighted losses)
to improve accuracy over individual classification models.
The ensemble technique alleviates over-fitting due to class
imbalance in the dataset, achieving a Balanced Multi-class
Accuracy (BMA) score of 0.591 without unknown class detection. The algorithm was extended by appending the proposed CS-KSU module collection to detect the presence of
images belonging to novel classes during test time. The extended algorithm secured an Area Under the ROC Curve
(AUC) score of 0.544 for the unknown class. Our algorithm’s performance is at par with the current state-of-theart for this task1
.
