Developing an innovative lung cancer detection model for accurate diagnosis in AI healthcare systems.
Authors
Affiliations (7)
Affiliations (7)
- School of Artificial Intelligence, Neijiang Normal University of Sichuan, Neijiang, Sichuan, 641100, China.
- Institute of Telecommunications, Computer Science and Photonics, Scoula Superiore Sant'Anna (SSSA), Pisa, Via Moruzzi 1, 56124, Italy. [email protected].
- International Centre for Wavelet Analysis and Its Applications, Big Data Research Institute, University of Electronic Science andTechnology of China (UESTC), Chengdu, Sichuan, China. [email protected].
- Department of Computer Science, Mohi-Ud Din Islamic University, Azad Jammu and Kashmir, 100600, Pakistan.
- Information Technology Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia.
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
- Department of Computer Science, Faculty of Science, Northern Border University , Arar, 73213, Kingdom of Saudi Arabia.
Abstract
Accurate Lung cancer (LC) identification is a big medical problem in the AI-based healthcare systems. Various deep learning-based methods have been proposed for Lung cancer diagnosis. In this study, we proposed a Deep learning techniques-based integrated model (CNN-GRU) for Lung cancer detection. In the proposed model development Convolutional neural networks (CNNs), and gated recurrent units (GRU) models are integrated to design an intelligent model for lung cancer detection. The CNN model extracts spatial features from lung CT images through convolutional and pooling layers. The extracted features from data are embedded in the GRUs model for the final prediction of LC. The model (CNN-GRU) was validated using LC data using the holdout validation technique. Data augmentation techniques such as rotation, and brightness were used to enlarge the data set size for effective training of the model. The optimization techniques Stochastic Gradient Descent(SGD) and Adaptive Moment Estimation(ADAM) were applied during model training for model training parameters optimization. Additionally, evaluation metrics were used to test the model performance. The experimental results of the model presented that the model achieved 99.77% accuracy as compared to previous models. The (CNN-GRU) model is recommended for accurate LC detection in AI-based healthcare systems due to its improved diagnosis accuracy.