  Image segmentation and classification are the two main fundamental steps in
pattern recognition. To perform medical image segmentation or classification
with deep learning models, it requires training on large image dataset with
annotation. The dermoscopy images (ISIC archive) considered for this work does
not have ground truth information for lesion segmentation. Performing manual
labelling on this dataset is time-consuming. To overcome this issue,
self-learning annotation scheme was proposed in the two-stage deep learning
algorithm. The two-stage deep learning algorithm consists of U-Net segmentation
model with the annotation scheme and CNN classifier model. The annotation
scheme uses a K-means clustering algorithm along with merging conditions to
achieve initial labelling information for training the U-Net model. The
classifier models namely ResNet-50 and LeNet-5 were trained and tested on the
image dataset without segmentation for comparison and with the U-Net
segmentation for implementing the proposed self-learning Artificial
Intelligence (AI) framework. The classification results of the proposed AI
framework achieved training accuracy of 93.8% and testing accuracy of 82.42%
when compared with the two classifier models directly trained on the input
images.
