Enhancing Lung Cancer Prediction using Alexnet

Lung cancer is one of the deadliest cancers and is difficult to predict. Since lung cancer frequently results in the death of the affected individual, it is necessary to treat the tumour promptly and effectively. In major medical applications, image processing techniques are commonly used in today's advanced detection and treatment procedures, where it is vital to identify abnormal tissues in the desired images. Thus several techniques have been developed to detect lung cancer in the early stages of the disease. Therefore, a comparative evaluation of various techniques based on machine learning for the detection of lung cancer has been provided. The outcome of the proposed work which uses AlexNet for the detection of lung cancer, shows an overall accuracy of 90.8%. The CTscan images of smoker’s lung cancer are taken as input from the dataset repository. The developed system uses the unique algorithm i.e., AlexNet, for detecting if the lung is affected or not. Moreover, the experimental results indicate the accuracy and error rate of the algorithm.