  Skin cancer is the most common cancer in the existing world constituting
one-third of the cancer cases. Benign skin cancers are not fatal, can be cured
with proper medication. But it is not the same as the malignant skin cancers.
In the case of malignant melanoma, in its peak stage, the maximum life
expectancy is less than or equal to 5 years. But, it can be cured if detected
in early stages. Though there are numerous clinical procedures, the accuracy of
diagnosis falls between 49% to 81% and is time-consuming. So, dermoscopy has
been brought into the picture. It helped in increasing the accuracy of
diagnosis but could not demolish the error-prone behaviour. A quick and less
error-prone solution is needed to diagnose this majorly growing skin cancer.
This project deals with the usage of deep learning in skin lesion
classification. In this project, an automated model for skin lesion
classification using dermoscopic images has been developed with CNN(Convolution
Neural Networks) as a training model. Convolution neural networks are known for
capturing features of an image. So, they are preferred in analyzing medical
images to find the characteristics that drive the model towards success.
Techniques like data augmentation for tackling class imbalance, segmentation
for focusing on the region of interest and 10-fold cross-validation to make the
model robust have been brought into the picture. This project also includes
usage of certain preprocessing techniques like brightening the images using
piece-wise linear transformation function, grayscale conversion of the image,
resize the image. This project throws a set of valuable insights on how the
accuracy of the model hikes with the bringing of new input strategies,
preprocessing techniques. The best accuracy this model could achieve is 0.886.
