Detection and diagnosis of pancreatic tumor using deep learning-based hierarchical convolutional neural network on the internet of medical things platform

Abstract

Pancreatic cancer is one of the deadliest types of cancer and its prognosis is extremely poor in the present scenario. Automatic pancreatic tumor image segmentation is often provided by computer-aided screening (CAD), diagnosis and quantitative evaluations in radiology images such as CT and MRI. Tumor classification through these methods can also help to track, predict and endorse customized therapy as a part of effective treatment, without invasions of cancer. Nowadays, neural networks (NN) have shown promising results for precise pancreatic image segmentation. This paper presents a deep learning-based Hierarchical Convolutional Neural Network (HCNN) for pancreatic tumor detection. A recurrent neural network (RNN) is provided to meet the issue of spatial discrepancy segmentation across slices of adjacent images. The recurrent neural network generates CNN results and fine-tunes its segmentation by enhancing the smoothness and shape. Furthermore, the HCNN training and configurations objectives have been illustrated to the performance of pancreatic tumor image segmentation. The experimental results demonstrate that the proposed approach can improve the performance of the classifier and reduce the cost on the Internet of medical things platform (IoMT).