Performance Evaluation of Machine Learning Algorithms for Lung Nodule Detection using Multi-Modal Imaging - A Hybrid Approach

Lung cancer is a major contributor to global mortality rates and identification is critical to improve patient outcomes. In recent years, machine learning algorithms have demonstrated promising results in identifying lung nodules from medical images. The most compelling area of research for scientists is the early detection of lung cancer. This study is a method for lung nodule detection using CT images. The study incorporates a hybrid model that combines multiple machine learning algorithms including CNN, SVM, DTC, ANN, and KNN to improve the accuracy of nodule detection. The hybrid model demonstrated high accuracy in identifying various types of lung nodules, including Adenocell carcinoma, squamous cell carcinoma, and large cell carcinoma. Specifically, the model achieved an accuracy rate of over 90% in detecting and differentiating normal lung tissue and Adenocell carcinomas. Accuracy graphs and priority setting were utilized to assess the model's capability in accurately predicting the presence of lung cancer. Additionally, the efficiency of the hybrid model was compared with other machine learning algorithms, including SVM, Random Forest, and Decision Trees. A large dataset of CT scans was collected for training and evaluation purposes. The results demonstrated the advantages of the suggested hybrid model in terms of accuracy and efficiency. This study highlights the importance of early lung nodule identification using CT scans and demonstrates the effectiveness of the hybrid model in accurately identifying different types of lung nodules.