Deep-Learning based Melanoma Detection using Cloud Approach

¹ Associate Professor, Department of Computing Technologies, SRM Institute of Science and Technology, Potheri, Kattankulathur - 603203, Chengalpattu District, Tamil Nadu, India.
² Student, Department of Computing Technologies, SRM Institute of Science and Technology, Potheri, Kattankulathur - 603203, Chengalpattu District, Tamil Nadu, India.
³ Student, Department of Computing Technologies, SRM Institute of Science and Technology, Potheri, Kattankulathur - 603203, Chengalpattu District, Tamil Nadu, India.
⁴ Assistant Professor, Career Development Centre, SRM Institute of Science and Technology, Potheri, Kattankulathur - 603203, Chengalpattu District, Tamil Nadu, India.

¹ Corresponding author: pradeeps1@srmist.edu.in

Abstract

The aim of computer vision techniquesand deep learning in the era of digitalization is to derive valuable insights from them and generate novel understanding. This makes it possible to employ imaging to quickly diagnose and treat a variety of diseases. In the field of dermatology, deep neural networks are utilized to differentiate between images of melanoma and non-melanoma skin lesions. In this paper, we have emphasised two important aspects of melanoma detection research. The accuracy of classifiers is the first thing to take into account, even with very little modifications to the dataset's characteristics there will be a lot of difference in accuracy. We investigated transfer learning issues in this case. We propose that continual training-test iterations are necessary to create reliable prediction models based on the results of the initial study.The second argument is the need for a system with a flexible design that can accommodate changes to training datasets.Our proposal for creating and implementing a melanoma detection service that utilizes clinical and thermoscopic images involves the development and implementing a hybrid architecture that fuses fog, edge and cloud computing. In addition, this design should aim to decrease the duration of the ongoing retraining process, which is necessary to accommodate the large volume of data that requires evaluation. This notion has been reinforced by experiments using a single computer and a variety of distribution techniques, which show how a dispersed strategy ensures output attainment in a noticeably more sufficient amount of time.