LCDctCNN: Lung Cancer Diagnosis of CT scan Images Using CNN Based Model
This work addresses early diagnosis of lung cancer, a deadly disease, but is incremental as it applies existing CNN methods to medical imaging data.
The authors tackled lung cancer detection from CT scan images using a CNN-based model, achieving an accuracy of 92%, AUC of 98.21%, recall of 91.72%, and loss of 0.328, outperforming other models like Inception V3, Xception, and ResNet-50.
The most deadly and life-threatening disease in the world is lung cancer. Though early diagnosis and accurate treatment are necessary for lowering the lung cancer mortality rate. A computerized tomography (CT) scan-based image is one of the most effective imaging techniques for lung cancer detection using deep learning models. In this article, we proposed a deep learning model-based Convolutional Neural Network (CNN) framework for the early detection of lung cancer using CT scan images. We also have analyzed other models for instance Inception V3, Xception, and ResNet-50 models to compare with our proposed model. We compared our models with each other considering the metrics of accuracy, Area Under Curve (AUC), recall, and loss. After evaluating the model's performance, we observed that CNN outperformed other models and has been shown to be promising compared to traditional methods. It achieved an accuracy of 92%, AUC of 98.21%, recall of 91.72%, and loss of 0.328.