Lung Cancer Classification Using Deep Learning-Based Techniques

Cancer is currently the most dangerous sickness the world has to cope with. Finding malignant nodules inside the lungs is difficult, despite the fact that numerous methods have been employed. The process of recognizing and separating lung cancer tissues from medical pictures such as CT or MRI scans is known as lung cancer segmentation. This procedure is necessary for a precise lung cancer diagnosis and treatment planning. Lung cancer dissection can be made more accurate and automated with the help of computing technology. Cancer in lung is one of the primary reasons of demise universal. Timely recognition and accurate diagnosis are critical for improving patient outcomes. In this research study, the authors examine the use of three different DL (Deep Learning) classifiers, namely CNN (Convolutional Neural Networks), RNN (Recurrent Neural Networks), and SAE (Stacked Autoencoders) for the categorization of lung malignancy from CT (Computed Tomography) images. The performance of these models is compared in form of accuracy, sensitivity and specificity. The investigational outcomes show that the CNN model outperformed the other models with an accuracy value of 92.63-%, the sensitivity rate of 0.91 and specificity value of 0.89. The tool used for execution is MATLAB.