Abstract
This work aims to design and develop an artificial neural network (ANN) architecture for the classification of cancerous tissue in the lung. A sequential model is used for the machine learning process. ReLU and Sigmoid activation functions have been used to supply weights to the model. The present work encompasses detecting and classifying the tumor cells into four categories. The four types of lung cancer nodules are adenocarcinoma, squamous-cell carcinoma, large-cell carcinoma, and small-cell carcinoma. Computed tomography (CT) and Positron emission tomography (PET) scan DICOM images are used for the classification. The proposed approach has been validated with the subset of the original dataset. A total of 6500 images have been taken in the experiment. The approach is to feed the CT scan images into ANNs and classify the image as the correct type. The dataset is provided by The Cancer Imaging Archive (TCIA). The dataset is titled “A Large-Scale CT and PET/CT Dataset for Lung Cancer Diagnosis.” The tumor cells are classified using the ANN architecture with 99.6% of validation accuracy and 4.35% loss.
Published Version
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