Abstract
Lung Cancer is the deadliest type of cancer. For diagnosing and detecting lung cancer, many Deep learning techniques are proposed. Deep learning techniques depend on massive data and extracting 3D features of images, which is a tedious task. Therefore deep transfer learning is used as an alternative for better results in medical image analyses like lung cancer, where pre-trained deep learning models are employed, which resolves the problem of labeled data scarcity. As no systematic survey is available in the literature, this paper reviews Lung Cancer detection and diagnosis using Deep Transfer Learning techniques. Moreover, all such methods are compared based on performance parameters like accuracy, Recall, F1- score and precision, etc. The comparison shows that the overall performance of deep learning techniques with the use of Transfer learning is enhanced.
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