Investigating Deep Learning Methods for Detecting Lung Adenocarcinoma on the TCIA Dataset

Rafia Jabbar Memon; Sammer Zai; Moazzam Jawaid; M Ahsan

Authors

Rafia Jabbar Memon Mehran University of Engineering and Technology.
Sammer Zai Mehran University of Engineering and Technology.
Moazzam Jawaid Mehran University of Engineering and Technology.
M Ahsan Mehran University of Engineering and Technology.

Keywords:

Adenocarcinoma, Lung Cancer, Convolutional Neural Network, Accuracy, Detection

Abstract

Lung cancer, one of the deadliest diseases worldwide, can be treated, where the survival rates increase with early detection and treatment. CT scans are the most advanced imaging modality in clinical practices. Interpreting and identifying cancer from CT scan images can be difficult for doctors. Thus, automated detection helps doctors to identify malignant cells. A variety of techniques including deep learning and image processing have been extensively examined and evaluated. The objective of this study is to evaluate different transfer learning models through the optimization of certain variables including learning rate (LR), batch size (BS), and epochs. Finally, this study presents an enhanced model that achieves improved accuracy and faster processing times. Three models, namely VGG16, ResNet-50, and CNN Sequential Model, have undergone evaluation by changing parameters like learning rate, batch size, and epochs and after extensive experiments, it has been found that among these three models, the CNN Sequential model is working best with an accuracy of 94.1% and processing time of 1620 seconds. However, VGG16 and ResNet50 have 95.0% and 93% accuracies along with processing times of 5865 seconds and 9460 seconds, respectively.

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