Deep Learning Based Patient Care Mobile Application for Detecting Skin Cancer

Authors

  • M. Mercy Jemima Research Scholar, Vivekanandha College of Arts and Sciences for Women (A), Tiruchengode, Tamilnadu, India. Author
  • Dr. J.K. Kanimozhi Associate Professor, PG & Research Department of Computer Science, Vivekanandha College of Arts and Sciences for Women (A), Tiruchengode, Tamilnadu, India. Author

DOI:

https://doi.org/10.47392/IRJAEH.2024.0003.i1

Keywords:

Transfer Learning, Skin Cancer, Machine Learning, CNN, Deep Learning

Abstract

Over time, skin cancer has become one of the most lethal malignancies in humans. Melanoma is the most dangerous type of skin cancer and is difficult to anticipate. Early identification is critical for maximising patients' chances of survival and preventing cancer from spreading to other areas of the body. It is very important to found and treat melanoma, before it spreads to the lymph nodes. So, the early finding is very vital. Skin cancer is normally diagnosed visually; it begins with physical examination, followed by dermoscopy, biopsy and histopathology, which takes several days. The major issue with melanoma is that, the disease can pave to some other cells. Laboratory sampling may cause the increase of lesion. Therefore, there must be an application that can perform the fastest, most accurate and cheapest diagnose. Computer based diagnosis can get better skin cancer diagnosis and it will work effectively to the disease symptoms. Our solutions will create a mobile cloud computing application that will take photos of the affected tumor and returns showing whether it is malignant or benign. Our solution will build a mobile application on cloud computing and it takes the pictures of affected skin tumors and gives result as whether it is affected as either Malignant or Benign.

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Published

2024-01-30

How to Cite

Deep Learning Based Patient Care Mobile Application for Detecting Skin Cancer . (2024). International Research Journal on Advanced Engineering Hub (IRJAEH), 2(01), 14-19. https://doi.org/10.47392/IRJAEH.2024.0003.i1

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