Using VGG16 Algorithms for classification of lung cancer in CT scans Image
This work addresses early lung cancer diagnosis for medical professionals, but it is incremental as it uses an existing method (VGG16) on new medical data.
The paper tackled lung cancer detection from CT scans by applying the VGG16 deep learning algorithm to classify nodules as malignant, benign, or healthy, achieving 91% accuracy, 92.08% sensitivity, and a 93% AUC.
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%.