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Lung cancer is one of the leading causes of cancer related deaths. It is due to the complexity of early detection of nodules. In clinical practice, radiologists find it difficult to determine whether a condition is normal or abnormal by manually analysing CT scan or X-ray images for nodule identification. Currently, various deep learning techniques have been developed to identify lung nodules as benign or malignant, but each technique has its own advantages and drawbacks. This work presents a thorough analysis based on segmentation techniques, Related features-based detection, multi-step detection, automatic detection, and deep convolutional neural network techniques. Performance comparison was conducted on a selected works based on performance measures. A potential research direction for the recognition of lung nodules is given at the end of this study.

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In present scenario, 72% of all fatalities globally are caused by lung cancer, which is the cellular fission of aberrant cells within the lungs. With a 19% likelihood of survival, lung cancer is also known as one of the major causes of death. A number of techniques, including X-rays, CT scans, biopsies, and others, can be used to diagnose tumors. The Computer Tomography (CT) scan technique is one of the most effective methods for detecting lung cancer early among the methods mentioned above. An early and precise diagnosis is crucial for the disease’s treatment. The creation of multiple machine learning algorithms can effectively forecast outcomes. The accuracy of the model in classifying diseases, however, is significantly influenced by the model’s complexity and the decision of the appropriate machine learning approach for the given dataset. To address this issue, this work develops a Fuzzy-based Intelligent Model for Lung Cancer Classification (FID-LCC). The wavelet cleft fuzzy algorithm can be used in the model to aggregate the features from the obtained images. Then, using Improved Binary Particle Swarm Optimization (IBPSO), the anomalous features can be selected. Following that, classification is accomplished using convolutional neural networks (CNN). The simulation findings demonstrate that the proposed strategy’s accuracy in determining the Lung Cancer is greater to other traditional methods when the classifier’s performances are compared.

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