Comprehensive and Comparative Framework for Lung Cancer Detection Using CNN

Lung cancer is one of the most lethal cancer types; thousands of peoples are infected with this type of cancer, and if they do not discover it in the early stages of the disease, then the chance of surviving of the patient will be very poor. For the suggested reasons above and to help in overcoming this terrible, early diagnosis with the assistance of artificial intelligence procedures most needed. Through this research, a Computer-aided system introduced for detecting lung cancer in a dataset by using a convolutional neural network technique for helping with the diagnosis of the patient's cases: normal or abnormal. The CNN with PCA approach as a promising solution to address these challenges. By combining the strengths of CNNs for image analysis and PCA for feature extraction, the project seeks to develop a robust and efficient lung cancer detection system. The proposed method is compared with the baseline method (SVM). The method Support Vector Machine achieved accuracy upto 75.0%. Finally The results also show that convolutional neural networks(CNN) outperforms the other Machine Learning algorithms. The proposed model gives high accuracy upto 98.548%.