Deep learning convolutional neural network (CNN) With Gaussian mixture model for predicting pancreatic cancer

• Published in: Multimedia tools and applications Vol. 79; no. 15-16; pp. 10233 - 10247
• Main Authors: Sekaran, Kaushik, Chandana, P., Krishna, N. Murali, Kadry, Seifedine
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2020; Springer Nature B.V
• Subjects: Algorithms; Artificial neural networks; Automation; Body parts; Cancer; Computed tomography; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Deep learning; Diagnosis; Experimentation; Health; Medical imaging; Medical research; Multimedia Information Systems; Neural networks; Pancreas; Pancreatic cancer; Positron emission; Predictions; Probabilistic models; Special Purpose and Application-Based Systems; Stomach; Tomography; TCIA; Gaussian mixture model (GMM); Expectation-Maximization algorithm (EM); Convolutional neural network (CNN)
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-019-7419-5

The tremendous research towards medical health systems are giving ample scope for the computing systems to emerge with the latest innovations. These innovations are leading to the efficient implementations of the medical systems which involve in automatic diagnosis of the health related problems. The most important health research is going on towards cancer prediction, which has different forms and can be affected on different portions of the body parts. One of the most affected cancer that predicted to be incurable are Pancreatic Cancer, which cannot be treated efficiently once identified, in most of the cases it found to be unpredictable as it lies in the abdomen region below the stomach. Therefore the advancements in the medical research is trending towards the implementations of an automated systems which identifies the stages of cancer if affected and provide the better diagnosis and treatment if identified. Deep learning is one such area which extended its research towards medical imaging, which automates the process of diagnosing the problems of the patients when appended with the set of machines like CT/PET Scan systems. In this paper, the deep learning strategy named Convolutional Neural network (CNN) model is used to predict the cancer images of the pancreas, which is embedded with the model of Gaussian Mixture model with EM algorithm to predict the essential features from the CT Scan and predicts the percentage of cancer spread in the pancreas with the threshold parameters taken as a markers. The experimentation is carried out on the CT Scan images dataset of pancreas collected from the Cancer Imaging Archive (TCIA) consists of approximately 19,000 images supported by the National Institutes of Health Clinical Center to analyze the performance of the model.