The Effect of Fully Connected Layers in Different CNN Architectures for Lung Cancer Analysis

EasyChair Preprint no. 11293

Abstract

 Improving patient outcomes requires early and precise lung cancer classification. Deep learning has been shown to be successful in the interpretation of medical pictures, particularly when convolutional neural networks (CNNs) are used. In this paper, we proposed a deep modified CNN network using three pre-trained models (Densenet169, MobileNetV2, and Resnet50V2) to improve lung cancer classification performance based on the IQ-OTH/NCCD Lung Cancer dataset. The experimental results show that our modified Densenet169 based strategy outperformed existing methods, earning the highest accuracy rates for lung cancer classification. Early identification of lung cancer can enhance patient prognosis and treatment options dramatically. This report outlines future research and advancement opportunities in this vital topic.

Keyphrases: Classification, CNN networks, deep learning, Lung Cancer, Transfer Learning

@Booklet{EasyChair:11293,
  author = {Zahia Benamara and Soraya Zehani and Athmane Zitouni},
  title = {The Effect of Fully Connected Layers in Different CNN Architectures for Lung Cancer Analysis},
  howpublished = {EasyChair Preprint no. 11293},

  year = {EasyChair, 2023}}