• Seema Bhalavi
• Prof. Shatendra Dubey

This paper present Lung cancer is a prevalent and deadly disease worldwide. Early detection is crucial for successful treatment and improved patient outcomes. This research presents a novel approach to lung cancer detection using a modified neural network combined with advanced image processing techniques, including the integration of robot-assisted feature selection. The system is designed to enhance the accuracy and efficiency of lung cancer diagnosis by analyzing MRI and CT scan images. The proposed system starts with the acquisition of lung images from MRI and CT scans. Subsequently, advanced image processing techniques are applied to enhance the quality of the images, remove noise, and improve feature extraction. This pre-processing stage ensures that the input data is optimal for subsequent analysis. Robot-assisted feature selection plays a crucial role in improving the performance of the system. Robots are employed to scan the lung images and identify relevant features indicative of potential cancerous regions. This collaborative approach helps reduce human error and increases the system's efficiency.

Lung cancer, Detection, Computed Tomography (CT), Image Processing Etc.

"LUNG CANCER DETECTION SYSTEM USING MODIFY NEURAL NETWORK WITH ROBUST FEATURES SELECTION IN MRI CT SCAN IMAGE PROCESSING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.a140-a148, April-2024, Available :http://www.jetir.org/papers/JETIR2404020.pdf

"LUNG CANCER DETECTION SYSTEM USING MODIFY NEURAL NETWORK WITH ROBUST FEATURES SELECTION IN MRI CT SCAN IMAGE PROCESSING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppa140-a148, April-2024, Available at : http://www.jetir.org/papers/JETIR2404020.pdf