Classification of Lung Cancer Subtypes Using Deep Learning Model

Abstract

Cancer is a leading cause of death worldwide, af- fecting millions of people each year. There is an urgent need for improved cancer detection, diagnosis, and treatment methods. Histopathological examination, involving the microscopic analysis of tissue samples, is the gold standard for cancer diagnosis. However, this process can be time-consuming and subjective, relying heavily on pathologists’ expertise. Deep learning models, particularly convolutional neural networks (CNNs), excel at image analysis and pattern recognition. CNNs can be trained on large datasets of histopathological images to learn the complex features associated with different cancer types. Once trained, these models can automate cancer detection, classify cancer subtypes, segment tumor regions and predict treatment response. Deep learning models, particularly convolutional neural networks (CNNs), have successfully classified various cancer subtypes. For instance, studies have shown the effectiveness of CNN, CNN Gradient Descent, VGG16, VGG-19, Inception V3, and Resnet-50 in accurately classifying lung cancer subtypes from histopathological images. Transfer learning, a technique that adapts pre-trained CNN models to new tasks, has further enhanced classification accuracy, especially when working with limited medical image datasets. The ability to accurately classify cancer subtypes using deep learning can aid pathologists in making more informed diagnoses and guide treatment strategies. Continued research and development in this field promise to revolutionize cancer diagnosis and prognosis, leading to more personalized and effective treatment strategies