Improving Accuracy of Lung Nodule Classification Using AlexNet Model

Lung Cancer is the world's fastest-growing cancer & is detected mainly at an early stage. Various modalities of medical imaging, such as computed tomography (CT) have been employed to reduce delays in diagnosis. So far, numerous machine learning architectures have been used by researchers to categorize lung nodules captured in CT scans into benign or cancerous. In this article, we proposed a novel 8-layer two-architecture of a three-dimensional deep convolutional neural network called AlexNet to classifying benign & malignant nodules from CT-Scan images. The Deep neural network extracts the features automatically. We apply binary cross-entropy to our proposed network's loss functionimprovetraining precision and validation accuracy of the model with 99% and 97% respectively.