Abstract
Lung cancer is unquestionably a lung-influencing chronic condition that significantly hampers the respiratory system. It is the second most dangerous disease which causes increase in death rate. To resolve this issue, we had planned to create a very Convolutional Neural Network using Transfer learning to specifically classify the lungs CT scans as normal, malignant, or benign in a subtle way. A dataset of 1100 lung CT scans is used for this purpose. For the most part, five Transfer Learning architectures are compared extensively in this classification such as MobileNet, VGG16, VGG19, DenseNet-201 and ResNet-101. Out of which, DenseNet-201 performed the best. The proposed strategy achieved a mean accuracy of 53 percent in the trials and 43% of mean F1-score, mean precision and mean recall.
Highlights
Lung Cancer is the second most normal malignancy in both males and females
This paper investigates the use of transfer learning in the task of detecting lung cancer from computed tomography (CT) scans
F1-score is the measure that is evaluated for this proposed system based on the aforementioned reports because the system deals with uneven distribution of number of photos in dataset categories
Summary
Lung Cancer is the second most normal malignancy in both males and females. If not identified at the early phase, it can cause passing. As per WHO, 2.21million cases were recorded in 2020, though 1.80 million passed on because of lung cancer [1]. Lung cancer develops when cells in the lungs multiply uncontrolled way. These can impair a person's breathing and spread to other parts of the body. Smoking and alcohol use are by far the leading causes of lung cancer. Lung cancer in non-smokers can be caused by radon, second-hand smoke, environmental pollution, or other aspects [2]. If cancer is present in another part of the body, it may affect the lungs
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