Classification of Lung and Colon Cancer using Stacked Ensemble Learning of Multiple CNN Architectures
Keywords:
Lung cancer, Colon cancer, classification, Xception,MobileNetAbstract
The two most prevalent and deadly types of cancer globally are lung and colon. Early and accurate detection of these cancers is necessary for improving patient outcomes. It was found that machine learning techniques, especially deep learning models, have the ability to generate accurate detection and classification results when trained on big databases. This paper introduces a novel framework to classify five classes of lung and colon cancer using ensemble learning of multiple Convolutional Neural Network (CNN) architectures. First, these models are trained and the predictions resulting from applying them on the test subset of images are preserved, then an ensemble voting is used to select the best result from each model. Our approach was applied on a challenging dataset of 25000 images. The classification accuracy achieved was 98.47% which is higher than the accuracy achieved using the standalone models applied to the same dataset. The potential of our ensemble approach can be further improved in the future.
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