• Shradha Shrivastava,
• Prof. Ruchi Dronawat
• Dr. Ritu Shrivastava M.

Nowadays nanotechnology is gaining more advantages and widely used in many real-life applications including minute tumor detection and effective diagnosis. Nanoscale imaging technique significantly increases the precision, accuracy of tumor detection, and classification of the tumor into benign or malignant. X-rays & Computerized Tomography (CT) scans are important for lung cancer diagnosis and research because it gives accurate segmentation results. To detect the presence of lung cancer, the basic steps to be followed are preprocessing and segmentation. Preprocessing is required to ensure that the dataset is consistent and displays only relevant features information. Regarding collect the data of available lung cancer images, the tumor type, and the efficiency measure of the dataset preprocessing techniques are applied to make the segmentation more accurate. Further, the lung tumor boundary area is measured by using a nanotechnology-based detection scheme and then the convolution neural network classifier is used to classify the lung cancer image.

Lung Disease, Cancer, Convolution Neural Network

"Optimization Accuracy of Lung Diseases Prediction using Convolution Neural Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 7, page no.e796-e802, July-2022, Available :http://www.jetir.org/papers/JETIR2207500.pdf

"Optimization Accuracy of Lung Diseases Prediction using Convolution Neural Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 7, page no. ppe796-e802, July-2022, Available at : http://www.jetir.org/papers/JETIR2207500.pdf