Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Computer Vision and Pattern Recognition | Feedforward neural network,Skin lesion,Pattern recognition,Computer science,Skin cancer,Artificial intelligence,Melanoma,Deep learning,Artificial neural network,Cancer |
DocType | Volume | Citations |
Journal | abs/1703.04364 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| P. Mirunalini | 1 | 0 | 1.35 |
| Chandrabose Aravindan | 2 | 137 | 23.13 |
| Vignesh Gokul | 3 | 0 | 0.34 |
| S. M. Jaisakthi | 4 | 0 | 0.68 |