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

Rafia Jabbar ()
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Rafia Jabbar: Mehran University of Engineering and Technology

International Journal of Innovations in Science & Technology, 2023, vol. 5, issue 4, 746-759

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 imagescan be difficult for doctors. Thus, automated detection helps doctorstoidentify 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 studypresents 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 timesof 5865 seconds and 9460 seconds, respectively.

Keywords: Adenocarcinoma; Lung Cancer; Convolutional Neural Network; Accuracy; Detection (search for similar items in EconPapers)
Date: 2023
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