Modular Neural Network-Based Lung Cancer Detection Using MRI Image Processing

This research presents a novel modular neural network for the accurate detection of lung cancer using MRI imaging. The study utilizes MRI reports of patients, applying advanced image processing techniques and neural networks to determine the presence of lung cancer. To enhance the quality of MRI images for analysis, a grayscale function is employed, ensuring optimal image suitability. The neural fuzzy classification algorithm is implemented to extract key features such as contrast and energy—crucial indicators of lung cancer. Additionally, the Feature Extraction Algorithm is used to calculate image entropy, further aiding in diagnosis. By analyzing entropy, contrast, and energy values, the proposed method effectively identifies lung cancer, offering a promising approach for early detection.