Transfer Learning Based System for Melanoma Type Detection

Abstract

Skin disease is found in different sorts, for example, basal, squamous cell carcinoma and melanoma among which melanoma is one that is very difficult to predict. Finding melanoma at an early stage is crucial. Melanomas come in many forms and may display none of the typical warning signs. Early detection can vastly increase chances for the cure. Computer vision can assume significant part in Medical Image Diagnosis and it has been demonstrated by numerous existing frameworks. Here, we represent computer aided strategy for identification of type of melanoma utilizing the transfer learning techniques. The proposed model utilized pre-trained and transfer learning model to image net. The proposed model successfully classified three melanoma types, namely, Nodular melanoma, Lentigo maligna melanoma and Superficial spreading melanoma. Additionally, exact identification of irregular borders from melanoma skin lesions is clinically significant. A main challenge is deciding the specific lesion border. For resolving the issue, we have executed another technique to identify the border of an affected or cancerous area. A dataset consists of 2475 dermoscopic images to train and test algorithms. Performance of a proposed system gave very good results as far as sensitivity, accuracy, specificity, recall, fscore, and precision. At last, we have compared performance of different transfer learning techniques.

Topics & Concepts