Deep Learning for Early Skin Cancer Detection: Combining Segmentation, Augmentation, and Transfer Learning

Skin cancer, particularly melanoma, is one of the leading causes of cancer-related deaths. It is essential to detect and start the treatment in the early stages for it to be effective and to improve survival rates. This study developed and evaluated a deep learning-based classification model to classify the skin lesion images as benign (non-cancerous) and malignant (cancerous). In this study, we used the ISIC 2016 dataset to train the segmentation model and the Kaggle dataset of 10,000 images to train the classification model. We applied different data pre-processing techniques to enhance the robustness of our model. We used the segmentation model to generate a binary segmentation mask and used it with the corresponding pre-processed image by overlaying its edges to highlight the lesion region, before feeding it to the classification model. We used transfer learning, using ResNet-50 as a backbone model for a feedforward network. We achieved an accuracy of 92.80%, a precision of 98.64%, and a recall of 86.80%. From our study, we have found that integrating deep learning techniques with proper data pre-processing improves the model’s performance. Future work will focus on expanding the datasets and testing more architectures to improve the performance metrics of the model.