Sallar Khan, Sir Syed University of Engineering & Technology, Pakistan
Sadaan Shahid, Sir Syed University of Engineering & Technology, Pakistan
Meer Shehroze Ali, Sir Syed University of Engineering & Technology, Pakistan
Zain Sarfaraz, Sir Syed University of Engineering & Technology, Pakistan
Syed Mohsin Raza, Sir Syed University of Engineering & Technology, Pakistan
Melanoma is the deadliest form of skin cancer. It is a type of cancer that develops from the pigment-containing cells known as melanocytes-mutate and become cancerous. Even in such a modern-cultured society of today, it is a very difficult task to detect such a disease and there are quite a few applications or systems developed for these tasks, which provide services only through paid subscriptions. We intend to develop an application that detects melanoma without any subscriptions. Melanoma is a hazardous type of skin cancer that is usually curable if detected early. As the biopsy (diagnosis) of this disease is a bit expensive and time-consuming procedure. Therefore, to tackle this issue we will be developing an android application through which we can detect Melanoma through the image processing techniques with the assistance of Convolutional neural network algorithm (CNN) with a training accuracy of 98% and testing accuracy of about 94%.
Skin Cancer, Melanoma, Android Application, Health Care, CNN
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APA:
Khan, S., Shahid, S., Shehroze Ali, M., Sarfaraz, Z., & Mohsin Raza, S., (2020). A Premature Detection of Melanoma Using CNN. International Journal of Grid and Distributed Computing (IJGDC), ISSN: 2005-4262 (Print); 2207-6379 (Online), NADIA, 13(2), 65-76. doi: 10.33832/ijgdc.2020.13.2.05.
MLA:
Khan, Sallar, et al. “A Premature Detection of Melanoma Using CNN.” International Journal of Grid and Distributed Computing (IJGDC), ISSN: 2005-4262 (Print); 2207-6379 (Online), NADIA, vol. 13, no. 2, 2020, pp. 65-76. IJGDC, http://article.nadiapub.com/IJGDC/vol13_no2/5.html.
IEEE:
[1] S. Khan, S. Shahid, M. Shehroze Ali, Z. Sarfaraz and S. Mohsin Raza, "A Premature Detection of Melanoma Using CNN." International Journal of Grid and Distributed Computing (IJGDC), ISSN: 2005-4262 (Print); 2207-6379 (Online), NADIA, vol. 13, no. 2, pp. 65-76, December 2020.
IJGDC aims to facilitate and support research related to grid and distributed computing technology and its applications. Our Journal provides a chance for academic and industry professionals to discuss recent progress in the area of grid and distributed computing technology.
