Lung Cancer Detection and Improving Accuracy Using Linear Subspace Image Classification Algorithm

The ability to identify lung cancer at an early stage is critical, because it can help patients live longer. However, predicting the affected area while diagnosing cancer is a huge challenge. An intelligent computer-aided diagnostic system can be utilized to detect and diagnose lung cancer by detecting the damaged region. The suggested Linear Subspace Image Classification Algorithm (LSICA) approach classifies images in a linear subspace. This methodology is used to accurately identify the damaged region, and it involves three steps: image enhancement, segmentation, and classification. The spatial image clustering technique is used to quickly segment and identify the impacted area in the image. LSICA is utilized to determine the accuracy value of the affected region for classification purposes. Therefore, a lung cancer detection system with classification-dependent image processing is used for lung cancer CT imaging. Therefore, a new method to overcome these deficiencies of the process for detection using LSICA is proposed in this work on lung cancer. MATLAB has been used in all programs. A proposed system designed to easily identify the affected region with help of the classification technique to enhance and get more accurate results.