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Comparative Analysis and Automated Eight-Level Skin Cancer Staging Diagnosis in Dermoscopic Images Using Deep Learning
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Author(s): Auxilia Osvin Nancy V. (Department of Computer science and Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, India), P. Prabhavathy (Department of Computer science and Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, India), Meenakshi S. Arya (Department of Transportation, Iowa State University, USA)and B. Shamreen Ahamed (Deparment of Computer science and Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, India)
Copyright: 2023
Pages: 19
Source title:
Meta-Learning Frameworks for Imaging Applications
Source Author(s)/Editor(s): Ashok Sharma (University of Jammu, India), Sandeep Singh Sengar (Cardiff Metropolitan University, UK)and Parveen Singh (Cluster University, Jammu, India)
DOI: 10.4018/978-1-6684-7659-8.ch007
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Abstract
The challenge in the predictions of skin lesions is due to the noise and contrast. The manual dermoscopy imaging procedure results in the wrong prediction. A deep learning model assists in detection and classification. The structure in the proposed handles CNN architecture with the stack of separate layers that use a differential function to transform an input volume into an output volume. For image recognition and classification, CNN is specifically powerful. The model was trained using labeled data with the appropriate class. CNN studies the relationship between input features and class labels. For model building, use Keras for front-end development and Tensor Flow for back-end development. The first step is to pre-process the ISIC2019 dataset, splitting it into 80% training data and 20% test data. After the training and test splits are complete, the dataset has been given to the CNN model for evaluation, and the accuracy on each lesion class was calculated using performance metrics. The comparative analysis has been done on pretrained models like VGG19, VGG16, and MobileNet.
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