Early Prediction of Lung Cancers Using Deep Saliency Capsule and Pre-Trained Deep Learning Frameworks - Ramana, Kadiyala; Kumar, Madapuri Rudra; Sreenivasulu, K.; Gadekallu, Thippa Reddy; Bhatia, Surbhi; Agarwal, Parul; Idrees, Sheikh Mohammad (Frontiers in Oncology, 2022)
Lung cancer is the cellular fission of abnormal cells inside the lungs that leads to 72% of total deaths worldwide. Tumor is also recognized to be one of the leading causes of mortality, with a chance of survival of only 19%. Tumor can be diagnosed using a variety of procedures, including X-rays, CT scans, biopsies, and PET-CT scans. From the above techniques, Computer Tomography (CT) scan technique is considered to be one of the most powerful tool for an early diagnosis of the Lung cancers. Recently machine and deep learning algorithms has picked up peak energy and its aids in building the strong diagnosis and prediction system using CT scan images. But, achieving the best performances in diagnosis still remains in the darker side of the research. To solve this problem, this paper proposes novel saliency based capsule networks for better segmentation and employs the optimized pre-trained transfer learning for the better prediction of lung cancers from the input CT images. The integration of capsule-based saliency segmentation leads to the reduction eventually reduces the risk of computational complexity and overfitting problem. Additionally, hyperparameters of pretrained networks are tuned by the whale optimization algorithm to improve the prediction accuracy by sacrificing the complexity.The extensive experimentation carried out using the LUNA-16 and LIDC Lung Image datasets and various performance metrics such as accuracy, precision, recall, specificity and F1-score are evaluated and analyzed. DenseNet, AlexNet, and Resnets-50, Resnets-100, VGG-16 and Inception models are used for comparison. Experimental results demonstrates that the proposed framework has achieved the peak performance of 98.5% accuracy, 99.0% precision ,98.8% recall and 99.1% F1-score and outperformed the other models .