Publication Date

Spring 2019

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Electrical Engineering

Advisor

Birsen Sirkeci

Keywords

cancer detection, convolutional neural networks, data augmentation, hyperspectral imagery

Subject Areas

Electrical engineering

Abstract

Hyperspectral images are 3-D images, which contain data in hundreds of spectral bands as opposed to 2-D images, which contain data in at most 3 bands (red, green, and blue). Hyperspectral imagery was initially developed for remote sensing; however, recently, researchers have started to see its potential in medical diagnosis and cancer detection. Hyperspectral images provide massive amounts of data about the objects they are studying, and this causes challenges during information processing. Machine learning tools, such as convolutional neural networks (CNNs), are known to be successful in extracting features and classifying traditional 2-D images. This thesis proposes CNN architectures for processing hyperspectral data for colon cancer detection. Using data taken from a limited number of colon tissue samples, this thesis shows that the proposed CNN architecture can classify cancerous and noncancerous tissue samples utilizing hyperspectral information. The obtained results are compared to grayscale images of the same tissue samples, looking both at grayscales of the individual hyperspectral bands and panchromatic grayscale images in which the spectral bands are merged together. The CNN using the hyperspectral data shows advantages over the grayscale data, with a 5.6% improvement in accuracy and a 0.037 improvement in F1 score over the individual band grayscale images and a 21.7% improvement in accuracy and a 0.178 improvement in F1 score over the panchromatic grayscale images. The results are also compared to a K-nearest neighbor (KNN) classifier and a logistic regression (LR) classifier using the hyperspectral data, and the CNN shows advantages over both. The CNN has a 17.9% improvement in accuracy and a 0.141 improvement in F1 score over the KNN classifier and a 5% improvement in accuracy and a 0.061 improvement in F1 score over the LR classifier.

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