Cancer Diseases Prediction Using Multiple Transfer Learning based on CNN Algorithm - Maysaa H. Abdulameer; Ruwaida M. Yas; Huda H. Ali; Huda W. Ahmed (Eximia, 2024)
For many years, the most common types of cancers that causes death has been lung and brain cancer. In addition, they are very hard to treat once they have been spread in the late stages. Early cancer detection and diagnosis have become crucial for providing immediate and efficient care to those who are afflicted with the disease. To predict cancer, this process needs cancer image classification which is a difficult task in the recent improvement of computer-based diagnostics. The main objective of this research is to classify Computed-Tomography (CT) images of the lungs and brain as cancerous or not. Therefore, this research shows the possibility of using a convolutional-neural-network (CNN) for lung and brain cancer detection through transfer learning (TL) without the difficulty of specifying features for image-classification. The proposed TCNN is trained and tested from scratch using two public and one private dataset using the TL technique. The suggested transfer learning-based model attained an accuracy of 92.81%, 97.21%, and 99.40% for Scratch TCNN Model, Pre-Trained TCNN Model, and Transfer Pre-Pre-Trained Model in that order. Hence, the proposed method gives high accuracy and decreases the time of diagnosis and treatment.