A Skin Cancer Detection Interactive Application Based on CNN and NLP

Xuping Gong,Yuting Xiao

doi:10.1088/1742-6596/2078/1/012036

Abstract

Skin cancer is the most common cancer with several different types. According to current estimations, one in five Americans will develop skin cancer in their lifetime. Therefore, early diagnosis and treatment of it is of crucial significance. Several advanced image processing methods have been applied to predict skin cancer. However, few researchers utilize those methods to build an interactive application. In this work, we implemented an interactive skin cancer diagnosis website, combining the convolutional neural network (CNN) and natural language processing (NLP) technology. The neural network model uses four convolutional layers and dense layers respectively to improve the accuracy. Two max-pooling layers were used to reduce redundant information. To address the severe overfitting problem, we chose to utilize the batch normalization along with dropout layers. Based on our results, 0.9935 in accuracy and 0.0225 loss is realized for training data, and accuracy of 0.8393 and 0.6648 loss for testing data. Natural language processing (NLP) was used to implement a chatbot for interaction with users. We crawled skin cancer related questions and answers from Quora and used them to train our chatbot. Lastly, we combined CNN and NLP to build an interactive skin cancer diagnosis website. VUE.js and Django were used to build the front-end and back-end of our website. These results offer a guideline for combining artificial intelligence with not only medicine but also interactive network, which enables people to get medical care more easily.

Highlights

Skin cancer is a skin malignant tumor [1] with high incidence, which is the most common of all cancers
Since different kinds of skin cancers distinguish themselves from their appearances, it is possible to predict them using some advanced image processing methods, e.g., convolutional neural network (CNN), due to their excellent performance in different tasks
In summary, the CNN is utilized to detect and classify skin cancer with an interactive application based on natural language processing (NLP), i.e., users can upload pictures on the website to get detection results, treatment recommendations and some related information

Summary

Introduction

Skin cancer is a skin malignant tumor [1] with high incidence, which is the most common of all cancers. According to the different sources of tumor cells, it can be divided into several types. The ultraviolet radiation from sunlight is the foremost cause of the skin cancer. This disease is becoming increasingly common in the young generation because of tanning and other cancer-causing elements. It is vital to diagnose and treat the skin cancer in the early stage, especially for some high fatality rate types. Since different kinds of skin cancers distinguish themselves from their appearances, it is possible to predict them using some advanced image processing methods, e.g., convolutional neural network (CNN), due to their excellent performance in different tasks (e.g., medical image classification [2] and segmentation [3] etc.)

Methods

Results

Conclusion