Paper
1 December 2021 An application based on deep learning for cancer diagnosis
Rongxing Liu
Author Affiliations +
Proceedings Volume 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering; 120791P (2021) https://doi.org/10.1117/12.2623000
Event: 2nd IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 2021, Xi'an, China
Abstract
Over the years, cancer has been attracting great attention worldwide due to its widespread and lethality. People have been trying to predict cancer using the machine in the hope of early diagnosis and treatment. Experts and scientists have been examining the performance of both basic machine learning techniques and some advanced neural networks on cancer prediction. In this work, we used convolutional neural networks (CNN) to make predictions based on images from histopathologic scans, known for their specialty in image recognition. We chose Visual Geometry Group 16 (VGG16) as our model, and we ran two experiments, one using pretrained parameters and one without. For both models, we ran a total of 10 epochs and recorded the accuracy, precision, and recall on both the training data and the testing data. According to the experiment results, the model trained from scratch had a slightly higher accuracy, with the highest accuracy being 89.01%. In addition, we designed a user interface so that people can access it more conveniently. Users can upload their scanned images, and the model will make a prediction and inform the users. In addition, we also designed a Natural Language Processing (NLP) chatbot to guide the users
© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Rongxing Liu "An application based on deep learning for cancer diagnosis", Proc. SPIE 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 120791P (1 December 2021); https://doi.org/10.1117/12.2623000
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KEYWORDS
Data modeling

Cancer

Performance modeling

Convolution

Machine learning

Tumor growth modeling

Visual process modeling

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