Date of Award
12-2022
Document Type
Project
Degree Name
Master of Science in Computer Science
Department
School of Computer Science and Engineering
First Reader/Committee Chair
Dr. Jennifer Jin
Abstract
Lung cancer is the third most common cancer in the U.S. This research focuses on classifying lung cancer cells based on their tumor cell, shape, and biological traits in images automatically obtained by passing through the
convolutional layers. Additionally, I classify whether the lung cell is adenocarcinoma, large cell carcinoma, squamous cell carcinoma, or normal cell carcinoma. The benefit of this classification is an accurate prognosis, leading to patients receiving proper therapy. The Lung Cancer CT(Computed Tomography) image dataset from Kaggle has been drawn with 1000 CT images of various types of lung cancer. Two state-of-the-art convolutional neural networks (CNNs) architectures, NFNets and EfficientNetB4, are trained, validated, and tested over
CT-Scan images. The experiment analysis signifies that NFNets classifies lung cancer images with 96% accuracy and EfficientNetB4 with 94% accuracy in this study. With the increase in the size of the dataset, it is predicted that the accuracy will improve.
Recommended Citation
Ankoliya, Mohit Ramajibhai, "LUNG CANCER TYPE CLASSIFICATION" (2022). Electronic Theses, Projects, and Dissertations. 1577.
https://scholarworks.lib.csusb.edu/etd/1577