A GUI Based Application for Lung Cancer Diagnosis Using Comprehensive Analysis Deep Learning Approach

Abstract

Abstract: Lung cancer is the third most terrible cancer in the world. It has been among the leading causes of death in both adults and children from recent years. Early cancer detection can lead to better treatment options for patients. The focus of conventional feature extraction methods is either on low -level features or high-level features, with some manually created features being utilized to fill in the gaps. A feature extraction framework does not require handcrafted features can be created to close this gap via encoding/combining low-level and high-level characteristics. Due to its ability to fully explain both low-level and high-level information and its integration of the feature extraction stage into the self-learning process, deep learning is incredibly effective for feature representation. Consequently, in this study, we use deep learning algorithms to detect the presence of lung cancer without the need for several doctor consultations. A web application is created as a healthcare application where lung cancer is detected using an input x-ray image. To identify cancer, the implementation uses the VGG-16 classification algorithm. As a result, the presence of the disease can be predicted early. We can then take quick action to stop any additional repercussions, which saves time, money, and human error. Lung cancer and its presence are identified in this investigation.