Self-Learning AI Framework for Skin Lesion Image Segmentation and Classification

• URL: http://arxiv.org/abs/2001.05838v1
• Date: Sat, 4 Jan 2020 09:31:11 GMT
• Title: Self-Learning AI Framework for Skin Lesion Image Segmentation and Classification
• Authors: Anandhanarayanan Kamalakannan, Shiva Shankar Ganesan and Govindaraj Rajamanickam
• Abstract summary: To perform medical image segmentation with deep learning models, it requires training on large image dataset with annotation. To overcome this issue, self-learning annotation scheme was proposed in the two-stage deep learning algorithm. The classification results of the proposed AI framework achieved training accuracy of 93.8% and testing accuracy of 82.42%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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