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Skin Cancer Detection: A Survey

Bhavay Khatri

Heera Lal Public School, Delhi

1-3

Vol: 13, Issue: 1, 2023

Receiving Date: 2022-11-13 Acceptance Date:

2022-12-28

Publication Date:

2023-01-05

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http://doi.org/10.37648/ijrst.v13i01.001

Abstract

Due to a lack of awareness of its warning signs and preventative measures, skin cancer—one of the deadliest types of cancer—has seen a significant increase in mortality rates. Therefore, early detection at an early stage is essential to halting the spread of cancer. Although there are other types of skin cancer, melanoma is the most dangerous. However, melanoma patients have a 96% survival rate when detected early with straightforward and cost-effective treatments. The project aims to classify various kinds of skin cancer using image processing and machine learning. Melanoma is a type of skin cancer that can be fatal. If detected early, melanoma skin cancer can be completely treated. Because it directly correlates with death, early melanoma skin cancer detection is critical for patients. In this study, early melanoma skin cancer is detected and categorized using a variety of algorithms, including K-means clustering, neural networks, K-Nearest Neighbour, and Naive Bayes. The UK best omega replica watches online with Swiss movements are worth having!
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Keywords: skin cancer; Melanoma; neural network

References

  1. Arslan Javid, Muhammad Sadiq, Faraz Akram “Skin Cancer Classification Using Image Processing and Machine Learning” 2021 IEEE 18th International Bhurban Conference on Applied Sciences & Technology
  2. Minakshi Waghulde, Shirish Kulkarni, Gargi Phadke “Detection of Skin Cancer Lesion from Digital images with Image Processing Techniques” 2020 IEEE Pune Section International Conference MIT World Peace university
  3. Enakshi Jana, Dr. Ravi Subban “Research on Skin Cancer cell Detection using Image Processing” 2020 IEEE International Conference on Computational Intelligence and Computing research
  4. Mrs. D. A. Phalke and Ms. H. R. Mhaske, melanoma skin cancer detection and classification based on supervised and unsupervised learning
  5. Fahdil Alwa , skin cancer image classification using naïve bayes
  6. Vidya M and Dr. Maya V Karki, Skin Cancer Detection using Machine Learning Technique
  7. Mohd Anas ,Ram Kailash Gupta and Dr. Shafeeq Ahmad, Skin Cancer Classification Using K-Means Clustering
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