Advancing Lung Cancer Diagnosis Through Deep Learning and Grad-CAM-Based Visualization Techniques

Lung cancer is one of the leading causes of cancer-related deaths worldwide. The importance of early detection and treatment in enhancing patient outcomes cannot be overstated. However, due to the disease’s complexity, this can be difficult. Deep learning algorithms have demonstrated encouraging results in effectively identifying and predicting lung cancer in recent years. In this paper, the CNN model we propose was trained to classify lung cancer CT scan images as either malignant, benign, or normal and achieved high accuracy on test set data which is 99.47%. The gradient-weighted class activation map (Grad-CAM) technique was used to create a superimposed image and bounded box image to see the highlighted parts based on which the model has taken a decision to predict the class. This shows which parts of the images were paid more attention to by the model while predicting the class. The model also achieves an F1-score of 99% and precision and recall of 99.48 and 99.47%. The dataset used for this research is available online. Overall, this result exceeds the prior benchmark approach used with this dataset.