• Jeevika K S

Lung cancer is a highly perilous illness ranking as one of the primary causes of disease and death, particularly when diagnosed in its initial stages. It presents significant challenges, as it is often only discernible after it has already diffused. This study proposes a lung cancer prognostication framework that uses deep learning to enhance the accuracy of cancer forecasting and disease determination, thereby enabling personalized treatment approaches based on disease severity. It consists of various steps, including image preprocessing and segmentation of lung CT image features extracted from the segmented images. Three different models, namely a DCNN model, a DCDNN model, and an ANN model, were employed for image classification, and a deep convolutional neural network (DCNN) was employed to detect lung diagnosis based on the extracted feature evaluation results showing the best accuracy of 99.41% in accurately discerning the presence or absence of lung cancer. The GAN model generates realistic lung CT scan images by training a generator to produce authentic images, and a discriminator to distinguish between real and fake images. The outcome of the system depends on the quality of the data, and a well-trained DCNN through training, validation, and testing on diverse datasets is crucial to ensure the reliability and generalizability of the model.

Lung cancer detection, Deep learning, Deep convolutional neural network.

"Lung Cancer Detection with Deep Learning: A CT Image Classification Approach ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 8, page no.b772-b777, August-2023, Available :http://www.jetir.org/papers/JETIR2308193.pdf

"Lung Cancer Detection with Deep Learning: A CT Image Classification Approach ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 8, page no. ppb772-b777, August-2023, Available at : http://www.jetir.org/papers/JETIR2308193.pdf