A New Application in Cancer Diagnosis Based on Convolutional Neural Network

Cancer detection has become much more significant in recent times than it was before since the number of patients suffering from cancer is rising year by year. But the actual case is that not all cancerous symptoms can be detected even by those experienced physicians by looking at their patients' histopathologic images through their eyes. To improve this situation, we proposed a model based on convolutional neural networks to help classify the histopathologic images into 2 categories: benign and malignant. Our model, in this way, can extract main features from a certain image and learn from these specific features for classification. We then evaluate its performance on an online public dataset consisting of over two hundred thousand different training images and over fifty thousand validating images. Our model returns a relatively high accuracy on the test data which the dataset provides, and it does outperform some of the existing models in making the binary classification. In addition, we also designed a user interface to put our model into practice. With the help of this interface, users can simply upload an image from their local directory for our pre-trained model to process.