Predicting Lung Cancer Using ResNet-50 Deep Residual Learning Model using Relu-Memristor Activation Function

Abstract

Lung cancer continues to stand as a prominent contributor to deaths caused due to cancer throughout the world, underscoring the paramount importance of precise and timely detection methodologies. In this paper, we introduce a pioneering strategy aimed at forecasting lung cancer by utilizing the power of deep residual learning, coupled with a meticulous examination of medical images at the pixel level. The given methodology uses convolutional neural networks (CNNs), specifically the Residual Network (ResNet) architecture, to extract intricate features from lung images, while also emphasizing the importance of raw pixel values as input features. Our experimental results showcase the promise of this novel approach, demonstrating remarkable predictive performance in lung cancer detection. By incorporating pixel values and deep residual learning, our model achieves a high degree of accuracy, sensitivity, and specificity, surpassing existing methodologies. This research makes important contribution in the advancement of early detection of lung cancer, ultimately enhancing the recovery chances of patient and potentially minimizing the burden of this devastating disease.