An automated slice sorting technique for multi-slice computed tomography liver cancer images using convolutional network

An early detection and diagnosis of liver cancer can help the radiation therapist in choosing the target area and the amount of radiation dose to be delivered to the patients. The radiologists usually spend a lot of time in selecting the most relevant slices from thousands of scans, which are usually obtained from multi-slice CT scanners. The purpose of this paper multi-organ classification of 3D CT images of liver cancer suspected patients by convolution network. A dataset consisting of 63503 CT images of liver cancer patients taken from The Cancer Imaging Archive (TCIA) has been used to validate the proposed method. The method is a CNN for classification of CT liver cancer images. The classification results in terms of accuracy, precision, sensitivity, specificity, true positive rate, false negative rate, and F1 score have been computed. The results manifest a high validation accuracy of 99.1%, when convolution network is trained with the data augmented volume slices as compared to accuracy of 98.7% with that obtained original volume slices. The overall test accuracy for data augmented volume slice dataset is 93.1% superior to other volume slices. The main contribution of this work is that it will help the radiation therapist to focus on a small subset of CT image data. This is achieved by segregating the whole set of 63503 CT images into three categories based on the likelihood of the spread of cancer to other organs in liver cancer suspected patients. Consequently, only 19453 CT images had liver visible in them, making rest of 44050 CT images less relevant for liver cancer detection. The proposed method will help in the rapid diagnosis and treatment of liver cancer patients.