  Melanoma, a malignant form of skin cancer is very threatening to life.
Diagnosis of melanoma at an earlier stage is highly needed as it has a very
high cure rate. Benign and malignant forms of skin cancer can be detected by
analyzing the lesions present on the surface of the skin using dermoscopic
images. In this work, an automated skin lesion detection system has been
developed which learns the representation of the image using Google's
pretrained CNN model known as Inception-v3 \cite{cnn}. After obtaining the
representation vector for our input dermoscopic images we have trained two
layer feed forward neural network to classify the images as malignant or
benign. The system also classifies the images based on the cause of the cancer
either due to melanocytic or non-melanocytic cells using a different neural
network. These classification tasks are part of the challenge organized by
International Skin Imaging Collaboration (ISIC) 2017. Our system learns to
classify the images based on the model built using the training images given in
the challenge and the experimental results were evaluated using validation and
test sets. Our system has achieved an overall accuracy of 65.8\% for the
validation set.
