ABSTRACT
Cancer is a widespread and prevalent disease that affects people in every corner of the world. There are multiple forms of cancer, but lung cancer is one of the most commonly diagnosed forms, second only to breast and prostate cancer. Unfortunately, it also has the highest mortality rate compared to other cancers.All humans can be affected by lung cancer, and it can be life-threatening. Timely treatment through early diagnosis is crucial in reducing the risk of fatal outcomes. In this paper, we have made an effort to categorize lung cancer data as either benign or malignant tumors using various Deep Learning approaches. Our study aimed to distinguish between these two types of lung cancer using a sequential neural network. Initially, our model achieved an accuracy rate of 93% during experiments. However, by implementing transfer learning (TL) and incorporating features from the sequential model, we were able to improve accuracy to 95%. This improvement allowed for more accurate classification of lung cancer images.
