• Dr.Amjad Khan

Lung cancer is a hazardous disease which is the unrestricted growth of abnormal cells that can occur in one or both of the lungs. Identification of lung cancer is an efficient way to minimize the death rate of patients. It is a vital step to screen out the computed tomography (CT) images for pulmonary nodules for the diagnosis of lung cancer. The survival rate of lung cancer depends upon early identification of lung nodules which is a crucial process. However, due to the heterogeneity of the lung nodules and the complexity of the surrounding environment, robust nodule detection has been a challenging task. The use of machine learning to detect, predict, and classify disease has grown exponentially in the past few years, especially for complex tasks such as cancer detection and recognition. We propose a method for Lung nodule classification from Lung CT images by convolutional neural networks. This method eliminates the need of manual feature extraction which is a feedback of previous works. The network is fed with raw lung CT images from publicly available LIDC-IDRI dataset. Here, the lung images are classified into two classes such as benign and malignant. This classification is achieved with the help of AlexNet convolutional neural network. This method successfully classified the lung CT images into two classes and achieved 98% accuracy with comparatively less false positive rates.

Lung cancer, computed tomography, Alexnet, classification

"AlexNet for Lung Cancer nodule Classification ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.b770-b777, November-2024, Available :http://www.jetir.org/papers/JETIR2411194.pdf

"AlexNet for Lung Cancer nodule Classification ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. ppb770-b777, November-2024, Available at : http://www.jetir.org/papers/JETIR2411194.pdf