Leveraging the computing power of the cloud to assist with medical diagnosis can become an effective tool for doctors to provide more consistent and reliable care. Artificial neural networks detect patterns too complex to be recognized by humans and can be applied to breast mass malignancy classification when evaluating Fine Needle Aspirates (FNAs). This project teaches the cloud how to diagnose breast cancer by implementing a custom-crafted neural network that consumes FNA data collected by the University of Wisconsin to answer the question – is a mass malignant or benign?
Medical usage demands neural networks achieve accuracy with their diagnosis and reduce malignant false negatives. Building on data collected by the University of Wisconsin in the early 1990s, this project first evaluates three modern commercial neural network implementations. Information regarding potential indicators of breast cancer is quantified in the dataset; specifically, clump thickness, single epithelial cell size, bare nuclei, mitoses, and five other attributes. Each network accepts this input to optimize its hidden nodes and is tested with ten trials. With each trial, a randomly selected 10% of the dataset is used to assess the predictive power of the constructed neural network. These commercially created neural networks serve as a control group.
Development of a custom neural network weights malignant false negatives and allows for the identification of inconclusive samples (capacities not available in commercial products.) Additionally, more samples are needed to improve the predictive capability of the network; therefore, the network has been published in the cloud, allowing for global submissions and benefit. The cloud service is hosted in the Google App Engine.
The successfully implemented custom network is tested with 6,800 trials. To assure maximum training, each sample is run through ten trials evaluated by different networks trained against all other samples. The custom neural network achieved predictive success of 97.4% with 99.1% sensitivity to malignancy – substantially better than the evaluated commercial products. Out of the commercial products, two experienced consistent success while the third experienced erratic success. The sensitivity to malignancy for the custom network was 5% higher than the best commercial network’s sensitivity. This experiment demonstrates modern neural networks can handle outliers and work with unmodified datasets to identify patterns. In addition, when all data is used for training, the custom network achieves 100% success with only 4 inconclusive samples, proving the network is more effective with more samples. Additionally, 7.6 million trials were run using different training sample sizes to demonstrate the sensitivity and predictive success improves as the network receives more training samples.
The Global Neural Network Cloud Service for Breast Cancer may be ready to diagnose actual patients – more global participation is required to confirm the findings and increase the predictive success on blind samples.
(Editor’s note: I weep with joy knowing that the world’s problems are being tackled and solved by increasingly younger minds.)