A comprehensive review of deep learning in lung cancer

To provide the reader with a historical perspective on cancer classification approaches, we first discuss the fundamentals of the area of cancer diagnosis in this article, including the processes of cancer diagnosis and the standard classification methods employed by clinicians. Current methods for cancer diagnosis are deemed ineffective, calling for new and more intelligent approaches.

Using VGG16 Algorithms for classification of lung cancer in CT scans Image

Lung cancer is the leading reason behind cancer-related deaths within the world. Early detection of lung nodules is vital for increasing the survival rate of cancer patients. Traditionally, physicians should manually identify the world suspected of getting carcinoma. When developing these detection systems, the arbitrariness of lung nodules' shape, size, and texture could be a challenge. Many studies showed the applied of computer vision algorithms to accurate diagnosis and classification of lung nodules. A deep learning algorithm called the VGG16 was developed during this paper to help medical professionals diagnose and classify carcinoma nodules. VGG16 can classify medical images of carcinoma in malignant, benign, and healthy patients. This paper showed that nodule detection using this single neural network had 92.08% sensitivity, 91% accuracy, and an AUC of 93%.