Performance Evaluation of Support Vector Machines and AdaBoost-SVM for Lung Nodule Identification in Chest Radiographs

Due to its aggressiveness and the difficulties in detecting it in time, lung cancer is a leading cause of cancer-related deaths. Unfortunately, it is often detected at an advanced stage. Although it is a significant difficulty, early detection is essential for individual survival. Radiographs of the chest and computed tomography scans are the first lines of diagnostics. On the other hand, incorrect diagnoses could result from the possibility of benign nodules. Early on, it is especially difficult to differentiate benign nodules from malignant ones due to their extremely comparable characteristics. To address this problem, a novel AdaBoost-SVM model is suggested to improve the accuracy of malignant nodule diagnosis. Kaggle is the source of the dataset that is used to train the model. The proposed model exhibits a remarkable accuracy rate of 97.96 ^% , surpassing the performance of conventional SVM methods. This development imparts the potential for enhanced precision and dependability in the crucial initial phases of lung cancer diagnosis