Detection of Lung Cancer diseases Based on CT SCAN Images using Deep Learning

Abstract

Abstract: Increased health risks originate from changes in the natural surroundings, climate, and way of life. India is the leading nation in respiratory illness mortality. They were always the second-leading mortality cause globally in 2017 behind heart disease, claiming the lives of 1 million (958,000) Indians. Severe effects of lung cancer, especially death, can be avoided by timely detection and treatment. The better diagnostic technique at the present is a chest X-ray, which is necessary for clinical therapy. By using Deep Learning to detect lung diseases from chest X-rays, a person with lung disease may be able to save their own life. Lung tumor is yet another name for lung cancer. It is a malignant tumor illness that causes uncontrolled cellular proliferation in the lung tissue. The much more common sources of lung cancer by just using tobacco products and smoking. One of the most common causes of death in the entire world is lung cancer. The ResNet-50 (Residual Network) pre-trained Deep Learning model used for image classification of the Convolutional Neural Network is a very successful method. Neural network (also known as Convolution or CNN). ResNet-50, which now has 50 layers, has been trained using just a million images from the ImageNet database in 1000 various categories. The ability to forecast outcomes quickly and accurately tends to make this possible. This paper presents a practical strategy for just using deep learning to recognize lung problems. Its primary objective is to develop a method that will assist radiologists to identify lung problems. This will be especially beneficial in remote locations in which radiologists are in short supply. To evaluate the efficacy and correctness of various models for trying to identify lung cancer from chest x-ray image data, the RESNET 50 model is used.