Skin Cancer Detection and Classification

  • Nikhil Angurana
  • Anand Prem Rajan
  • Ishaan Srivastava
Keywords: MATLAB, Skin Cancer, Artificial Neural Network


Skin cancer is a term given to the uncontrolled growth of strange skin cells. It occurs whenever unrepaired DNA damages to skin cells trigger mutations, or any other genetic defects, that lead the skin cells to multiply readily and form malignant tumors. Image processing is a commonly used method for skin cancer detection from the appearance of the affected area on the skin. The input to the system is that the skin lesion image so by applying novel image process techniques, it analyses it to conclude about the presence of skin cancer. The Lesion Image analysis tools checks for the various Melanoma parameters Like Asymmetry, Border, Colour, Diameter, (ABCD rule), etc. by texture, size and form analysis for image segmentation and have stages. The extracted feature parameters are accustomed classify the image as traditional skin and malignant melanoma cancerlesion.

Artificial Neural Network (ANN) is one of the important branches of Artificial Intelligence, which has been accepted as a brand-new technology in computer science for image processing. Neural Networks is currently the area of interest in medicine, particularly in the fields of radiology, urology, cardiology, oncology, etc. Neural Network plays a vital role in an exceedingly call network. It has been used to analyze Melanoma parameters Like Asymmetry, Border, Colour, Diameter, etc. which are calculated using MATLAB from skin cancer images intending to developing diagnostic algorithms that might improve triage practices in the emergency department. Using the ABCD rules for melanoma skin cancer, we use ANN in the classification stage. Initially, we train the network with known target values. The network is well trained with 96.9% accuracy, and then the unknown values are tested for the cancer classification. This classification method proves to be more efficient for skin cancer classification.


Download data is not yet available.


Pehamberger H, Binder M, Steiner A, & Wolff K. (1993). In vivo epi luminescence microscopy: Improvement of early diagnosis of melanoma. Available at:

A. Bono, S. Tomatis, & C. Bartoli. (1999 Jan). The ABCD system of melanoma detection: A spectrophotometric analysis of the asymmetry, border, color, and dimension. Cancer, 85(1), 72–77.

Bafounta, ML, Beauchet A, Aegerter P, & Saiag P. (2001). Is dermoscopy (epi luminescence microscopy) useful for the diagnosis of melanoma? Results of a meta-analysis using techniques adapted to the evaluation of diagnostic tests. Arch Dermatol, 13(10), 1343-1350.

How to Cite
Nikhil Angurana, Anand Prem Rajan, & Ishaan Srivastava. (2019). Skin Cancer Detection and Classification. International Journal of Engineering and Management Research, 9(2), 111-114.