Deep Learning Approaches for Lung Cancer Detection: A Comprehensive Analysis of Models, Optimization Techniques, and Architectures

Abstract

Lung cancer continues to be a significant global health challenge, highlighting the urgent need for innovative methods for early detection and precise diagnosis. This paper provides an extensive review of various deep learning techniques applied to lung cancer detection using medical image datasets. We examine a broad spectrum of deep learning models, including sequential models, convolutional neural networks (CNNs), and several optimization algorithms like ADAM, SGD, and RMSPROP. The analysis begins with the use of sequential models for binary classification of lung cancer images, followed by an exploration of optimization strategies to enhance model performance. We then extend the discussion to multi-class classification, focusing on the different types of lung cancer. To ensure thorough model training and evaluation, random mini-batch evaluations are performed using Python Keras. Additionally, CNNs are employed for effective feature extraction and classification, leveraging spatial patterns in the imaging data. Alongside traditional architectures, we incorporate data augmentation and regularization techniques to prevent overfitting and improve the models' generalization ability. The research also explores a range of CNN architectures, including the widely recognized VGG model, to identify the most suitable configurations for lung cancer detection. Beyond conventional models, alternative deep learning methods such as recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and autoencoders are also considered. By determining the optimal approach, this study aims to enhance the accuracy and efficiency of lung cancer diagnosis, potentially leading to better patient outcomes and reduced mortality rates.