Modified ResNet model for medical image-based lung cancer detection

Abstract

Lung cancer is still the most common cause of tumor death in the world. Therefore, there is a great demand to develop diagnostic tools for lung cancer. This research proposes a diagnostically tuned modified ResNet 50 model for detecting and diagnosing lung cancer from chest X-ray images. The architecture of ResNet 50 is adapted to be more suitable for the unique challenges presented by medical imaging data. The modifications include adding extra batch normalization layers for stabilizing training, replacing fully connected layers with global average pooling to reduce overfitting, and adding a squeeze-and-excitation (SE) block that enhances the model's focus on key features such as nodules and lesions. Furthermore, transfer learning was performed on the pre-trained ResNet 50 weights, and the model was fine-tuned to the dataset of images of lungs for better sensitivity regarding cancerous patterns. This modified ResNet 50 was evaluated on a publicly available dataset of lung images from the JSRT dataset, which outperforms the original ResNet 50 and state-of-the-art research. The proposed model achieves high sensitivity, specificity, precision, F1-score and accuracy, which are considered the most important factors in clinical settings. Accuracy reached as high as 98.77% in the detection of lung cancer, as shown by the results. The results also show that the modified ResNet model can be a highly reliable and efficient tool for the early detection of lung cancer. As a result, the improved architecture leads to better diagnostic accuracy and reduced computational complexity so it can be used in medical imaging with real-time applications.