• Aliasgar Pardawala
• Ruttik Trymbake
• Tayyaba Pinjari
• Sakshi Jagtap

Early detection is important for successful treatment of melanoma skin cancer. Melanoma is also commonly considered to be the most dangerous form of skin cancer, since it is far more likely to spread to other areas of the body if not diagnosed and treated early. Medical machine vision alternatively referred to as non-invasive medical image processing, is gaining traction in the clinical diagnosis of a wide variety of diseases. Among these methods is an automated image processing method that enables the lesion to be assessed accurately and quickly. The steps in this s are as follows: collection of a database of dermoscopy images, preprocessing, segmentation via thresholding, statistical feature extraction via Gray Level Co-occurrence Matrix (GLCM), Asymmetry, Border, Color, and Diameter (ABCD), feature selection via Principal component analysis (PCA), calculation of the total Dermoscopy Score, and finally classification via Convocation neural network (CNN). According to the findings, the classification accuracy achieved is 92.1 percent.

EGO’s, donation type, donors.

"SKIN CANCER DETECTION USING CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 4, page no.h300-h302, April-2022, Available :http://www.jetir.org/papers/JETIR2204736.pdf

"SKIN CANCER DETECTION USING CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 4, page no. pph300-h302, April-2022, Available at : http://www.jetir.org/papers/JETIR2204736.pdf