Paper Contents
Abstract
Cancer is one of the most serious and widespread disease that is responsible for large number of deaths every year. Among all different types of cancers, lung cancer is the most prevalent cancer having the highest mortality rate . Early diagnosis of lung cancer is crucial to ensure curative treatment and increase survival rates. Computed tomography scans are used for identification of lung cancer as it provides detailed picture of tumor in the body and tracks its growth . Although CT is preferred over other imaging modalities, visual interpretation of these CT scan images may be an error prone task and can cause delay in lung cancer detection. In this study, we developed a computer-aided diagnosis system for automatic Lung Cancer detection using Lung CT Scan images. We employed deep transfer learning to handle the scarcity of available data and designed a Convolutional Neural Network (CNN) model and MobileNet model. The proposed approach was evaluated on publicly available lung cancer CT scan dataset. The outcome of the selected model decides whether the patient is infected with lung cancer or not . The proposed version generates a better response to the inputs to confirm the disease also helps to develop better treatments , increasing survival chance and quality of life for patients by detecting lung cancer which helps in the increase of patients survival rate.
Copyright
Copyright © 2023 Kondamuri Prasanna Devi. This is an open access article distributed under the Creative Commons Attribution License.
