Skin Cancer Classification Using CNN

Authors

  • Sneh Raj School of Electrical Engineering, Vellore Institute of Technology, India
  • Aviral Varshney School of Electrical Engineering, Vellore Institute of Technology, India
  • Abhishek Gudipalli School of Electrical Engineering, Vellore Institute of Technology, India

Keywords:

Skin Cancer, CNN, Mel, Histogram, Conv2D, MaxPool2D

Abstract

One of the most common illnesses on the planet is cancer. Cancer comes in many forms and affects different areas of the human body. Cancer kills nearly 5 million people each year, with skin cancer accounting for a significant proportion. The aberrant cell proliferation of the human body produced by the steady progression of physical, chemical, or biological carcinogens is known as skin cancer. Skin cancer is defined as aberrant skin cell proliferation that leads in neoplastic skin cell growth. There are four main types of skin cancer: actinic keratosis, basal cell carcinoma, squamous cell carcinoma, and melanoma. The mortality rate from melanoma skin cancer is significantly higher than that from non-melanoma skin cancer. There has been a consistent increase in both the incidence of melanoma and the number of cases analysed of non-melanoma skin cancers. Thus, skin cancer detection in its early stages is crucial for improving life expectancies. The goal is to use various forms of image processing and image recognition technologies such as segmentation and convolutional neural networks to create highly accurate and optimized versions of classifiers for various forms of skin cancer the categorization of skin problems is another highlight of this study.

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Published

24.11.2023

How to Cite

Raj, S. ., Varshney, A. ., & Gudipalli, A. . (2023). Skin Cancer Classification Using CNN. International Journal of Intelligent Systems and Applications in Engineering, 12(5s), 26–32. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3817

Issue

Vol. 12 No. 5s (2024)

Section

Research Article

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