Deep Derma Scan: A Proactive Diagnosis System for Predicting Malignant Skin Tumor with Deep Learning Mechanisms

Deep Derma Scan: A Proactive Diagnosis System for Predicting Malignant Skin Tumor with Deep Learning Mechanisms
  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-8
Year of Publication : 2022
Authors : T. Tamilselvi, C. Vijayakumaran, K. Revathi, A. Ponmalar
DOI : 10.14445/22315381/IJETT-V70I8P232

How to Cite?

T. Tamilselvi, C. Vijayakumaran, K. Revathi, A. Ponmalar, "Deep Derma Scan: A Proactive Diagnosis System for Predicting Malignant Skin Tumor with Deep Learning Mechanisms," International Journal of Engineering Trends and Technology, vol. 70, no. 8, pp. 310-317, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I8P232

Abstract
The transformation in DNA due to the continuous, unprotected exposure to ultraviolet rays and high toxic chemical management in occupational settings causes skin cancer in general. There exist two major types of skin cancer, namely melanoma and non-melanoma. The basal carcinoma and squamous carcinoma are categorized under the non-melanoma type of cancer. As per the recent statistical report by the world cancer research fund of the American institute of cancer research, melanoma skin is attributed as the nineteenth most commonly occurred cancer irrespective of age and gender worldwide. Also, it reveals that non-melanoma is the fifth most prevalent skin cancer worldwide, but it is less likely to grow, spread and be treatable. The early identification of the malignant skin tumor with periodic treatment increases the survival rate of melanoma patients. The initial screening and regular monitoring reduce the risks of becoming benign into a cancerous cell. The existing solutions are profound in this area, as mobile applications are insufficient to prove their accuracy in diagnosing. They missed out on large cases as unpredicted, even the physical examination by doctors. The deep learning technologies will enhance the performance of accurate prediction of malignant tumors in advance. In this work, an optimized deep neural network model is developed to predict skin cancer and evaluated against the most prevalent machine learning models.

Keywords
Deep learning, Image processing, Malignant tumor, Proactive diagnostic system, Skin cancer.

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