Impact Score 2.97
The substantial clinical trial indicates that early detection and diagnosis of cancer can provide patients with more flexible treatment schemes, and improved life quality and survivability. Therefore, more and more attention has been paid to related fields, e.g., intelligent diagnosis of early cancer with ultrasound tomography, x-ray tomography, protein mass spectrometry, and the genetic-analysis. By these means, the Computer-Aided Diagnosis (CAD) system provides additional information and support the decision-making on disease diagnosis and cancer staging, which will provide meaningful second opinions in clinical diagnosis.
Indeed, the traditional CAD model consists of feature extraction and machine learning-based classification, in which feature engineering is highly dependent on the cooperation of experienced engineers and medical workers. Meanwhile, the features of early cancer are more subtle and hard to detect.
Aiming to alleviate this issue, the artificial neural network has opened up a path towards effective feature extraction and precise diagnose.
Benefit from the significant progress of high-performance computational resources, big data technology, and advanced deep learning methods, the artificial neural network (ANN) has revolutionized the pattern of knowledge representation and yield fruitful results of applications, including but not limited to feature embedding, visual understanding, and numerical regression.
In addition, ANN models are data-driven and can be trained end-to-end. It attempts to integrate the feature extraction, feature selection, and intelligence decision into a supervised learning procedure. Thus, time and labor can be dramatically reduced in feature engineering.
The purpose of the special issue is to promote the development of the field of intelligent systems with neural networks for early cancer detection.
The neural network theory and models will empower the computer-aided medical diagnosis with intelligence and brings tremendous impact on people's lives. But a vast body of literature show that early cancer detection without manual intervention is almost impossible. That is, the NN-based early cancer detection is still in its infancy, and there is still a long way ahead in achieving the ultimate goal of using neural networks to facilitate the early detection practice without much manual intervention.
Topics of interest include but are not limited to the following:
Dr Shiping Wen (Lead Guest Editor), Professor, Faculty of Engineering & Information Technology, Core Member, AAII - Australian Artificial Intelligence Institute, University of Technology Sydney, Australia, [email protected]
Dr. R. Dhanasekaran, Director-Research, Syed Ammal Engineering College, India, [email protected]
Manuscript Submission: August 31, 2021Decision Notification (Reject/ minor/major revision): October 31, 2021Revised manuscript Due Date: December 31, 2021Final Decision: January 31, 2022
Authors from academia and industry working on the above research topics are invited to submit original manuscripts that have not been published and are not currently under review by other journals or conferences. Previously published conference papers should be clearly identified by the authors at the submission stage and an explanation should be provided about how such papers have been extended. At least 30% of new content is expected.
Papers should be prepared by following the instructions for authors of Neural Processing Letters at https://springer.com/11063, and the authors should submit their manuscript based on the following steps:
1. Submit manuscript on the submission website of Neural Processing Letters https://www.editorialmanager.com/nepl/default.aspx.
2. In the ‘Additional Information’ section, answer ‘Yes’ to the question ‘Does this manuscript belong to a special issue?’
3. Select ‘SI: Neural Networks for Early Cancer Detection’.
The review process will be done by following the standard review process of this journal with, in general, two reviewing rounds. After this, guest editors will make their initial decision and the EIC will send the final decision.