Detection of Lung Cancer using SVM Algorithm and Comparing With K-Means Accuracy

Abstract

Thousands of people are infected with lung cancer every year, and the patient's chances of survival are extremely low if the disease is not detected in its early stages. For the reasons already stated and to aid in overcoming this dreadful situation, early diagnosis with the use of AI methods is absolutely necessary. Using a convolutional neural network approach with AlexNet architecture, this research provided a computer-aided system for identifying lung cancer in a dataset obtained from Iraqi hospitals. The system helps with the identification of patient cases, whether they are normal, benign, or malignant. With a maximum accuracy of 93.548%, the suggested model is quite precise. Accuracy is 98.714 percent and specificity is 95.0 percent, two further performance criteria with very high results.