Paper Key : IRJ************119
Author: Achal Yogesh Jaiswal ,Aboli Diwan Sukhdeve,Sonu Ramdas Gaikwad,Kiran Deveshwar Shahare ,Prof. Pawan H. Raut
Date Published: 01 May 2023
Abstract
Skin cancer is one of the most dangerous diseases in the world. Correctly classifying skin lesions at an early stage could aid clinical decision-making by providing an accurate disease diagnosis, potentially increasing the chances of a cure before cancer spreads. However, achieving automatic skin cancer classification is difficult because the majority of skin disease images used for training are imbalanced and in short supply; meanwhile, the models cross-domain adaptability and robustness are also critical challenges. Recently, many deep learning-based methods have been widely used in skin cancer classification to solve the above issues and achieve satisfactory results. Nonetheless, reviews that include the abovementioned frontier problems in skin cancer classification are still scarce. Therefore, in this article, we provide a comprehensive overview of the latest deep learning-based algorithms for skin cancer classification. We begin with an overview of three types of dermatological images, followed by a list of publicly available datasets relating to skin cancers. After that, we review the successful applications of typical convolutional neural networks for skin cancer classification. As a highlight of this paper, we next summarize several frontier problems, including data imbalance, data limitation, domain adaptation, model robustness, and model efficiency, followed by corresponding solutions in the skin cancer classification task. Finally, by summarizing different deep learning-based methods to solve the frontier challenges in skin cancer classification, we can conclude that the general development direction of these approaches is structured, lightweight, and multimodal. Besides, for readers convenience, we have summarized our findings in figures and tables. Considering the growing popularity of deep learning, there are still many issues to overcome as well as chances to pursue in the future.Deep learning is a new research area within modern technology using microservices with big data, virtual reality and also augmented reality. Due to the development of huge computing capacity, technologies such as deep learning application using MobileNet (CNN) has revolutionized image classification. Deep learning can be used to classify the different types of skin cancer types. This learning technique uses different algorithms such as MobileNet CNN algorithms. MobileNet algorithms are suitable ways to recognize the images from the input and give accurate results. In this current work, MobileNet CNN is used in our data set to classify skin disease types according to our input.