Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.
SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The ISIC Challenge 2018 dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.
INTRODUCTION: The International Skin Imaging Collaboration (ISIC) is an international effort to improve melanoma diagnosis, sponsored by the International Society for Digital Imaging of the Skin (ISDIS). The ISIC Archive contains the most extensive publicly available collection of quality-controlled dermoscopic images of skin lesions. Since 2016, ISIC has sponsored annual challenges for the computer science community associated with leading computer vision conferences.
For this modeling project, we will predict whether an image represents one of seven disease conditions (Task 3: Lesion Diagnosis). In this iteration, we will construct a CNN model based on the EfficientNetB7 architecture to make predictions.
ANALYSIS: In this iteration, the EfficientNetB7 model’s performance achieved a categorical accuracy score of 87.10% after ten epochs using the training dataset. The same model processed the validation dataset with a categorical accuracy rate of 84.46%.
CONCLUSION: In this iteration, the EfficientNetB7-based CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.
Dataset Used: ISIC 2018 Challenge Dataset
Dataset ML Model: Multi-Class classification with numerical attributes
Dataset Reference: https://challenge.isic-archive.com/data
One potential source of performance benchmarks: https://challenge.isic-archive.com/leaderboards/2018
The HTML formatted report can be found here on GitHub.