Lung Nodule Classification Using Convolutional Autoencoder and Clustering Augmented Learning Method(CALM).
Year: 2020
Type: dataset
Abstract: Early detection of lung cancer can help in a sharp decrease in the lung cancer mortality rate, which accounts for more than 17% percent of total cancer-related deaths.A large number of cases are encountered by radiologists daily for initial diagnosis.Computer-Aided Diagnosis(CAD) systems can assist radiologists by offering a second opinion and making the whole process faster.However, one drawback of CAD systems is a large amount of data needed to train them, which can be expensive in the medical field.In this paper, we propose using a generative adversarial network(GAN) as a potential data augmentation strategy to generate more training data to improve CAD systems.We also propose a convolutional autoencoder deep learning framework to support unsupervised image features learning for lung nodule through unlabeled data.The paper also introduces Clustering Augmented Learning Method (CALM) classifier which is based on the concept of simultaneous heterogeneous clustering and classification to learn deep feature representations of the features obtained from Convolutional autoencoder.The classification model within CALM consists of a Feedforward Neural Net (FNN) architecture.To improve the accuracy of the classification model, CALM iterates between clustering and learning to form robust clusters, thereby leveraging the learning process of the FNN.Computational experiments using the National Cancer Institute (NCI) Lung Image Database Consortium (LIDC) dataset resulted in an overall accuracy of 95.3% with a precision of 94.9%.
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3013259916