Intelligent operating architecture for audio-visual Breast Self-Examination Multimedia Training System

This paper introduces the initiated development of a computerised system called Breast Self- Examination - Multimedia Training System (BSE-MTS) and ongoing research for its development into effective, intelligent, high-tech system. Firstly, it presents the major components of the BSE-MTS and describes its future development into an intelligent system. Then, it outlines the development of the system as an intelligent integration of various modules of heterogeneous information, i.e.: (1) domain knowledge and perception of breast structures, locations, nodules (2) graphic and visual information (3) speech recognition and speech synthesis in the specific domain (4) interactive audio-visual feedback to the users of the BSE-MTS. The authors performed tests on using BSEMTS and present the outcomes. The paper is result of a research study.

[1]  S. Rustamov,et al.  Human-computer dialogue understanding hybrid system , 2012, 2012 International Symposium on Innovations in Intelligent Systems and Applications.

[2]  Xu Wang,et al.  A Novel Acoustic Feature Extraction Algorithm Based on Root Cepstrum Coefficients and CCBC for Robust Speech Recognition , 2008, 2008 Second International Symposium on Intelligent Information Technology Application.

[3]  T. Ravichandran,et al.  A novel approach for speech feature extraction by Cubic-Log compression in MFCC , 2013, 2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering.

[4]  Elmer P. Dadios,et al.  Computer-aided BSE torso tracking algorithm using neural networks, contours, and edge features , 2014, TENCON 2014 - 2014 IEEE Region 10 Conference.

[5]  Elmer P. Dadios,et al.  Detecting and tracking female breasts using neural network in real-time , 2013, 2013 IEEE International Conference of IEEE Region 10 (TENCON 2013).

[6]  James H. Martin,et al.  Speech and Language Processing An Introduction to Natural Language Processing , Computational Linguistics , and Speech Recognition Second Edition , 2008 .

[7]  E. P. Dadios,et al.  Computer vision-based breast self-examination palpation pressure level classification using artificial neural networks and wavelet transforms , 2012, TENCON 2012 IEEE Region 10 Conference.

[8]  V. Voenílek Artificial Intelligence and GIS: Mutual Meeting and Passing , 2009, INCOS 2009.

[9]  Vit Vozenilek Artificial Intelligence and GIS: Mutual Meeting and Passing , 2009, 2009 International Conference on Intelligent Networking and Collaborative Systems.

[10]  Elmer P. Dadios,et al.  Depth estimation in monocular Breast Self-Examination image sequence using optical flow , 2014, 2014 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM).

[11]  Elmer P. Dadios,et al.  Hiligaynon language 5-word vocabulary speech recognition using Mel frequency cepstrum coefficients and genetic algorithm , 2014, 2014 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM).

[12]  Elmer P. Dadios,et al.  Hand initialization and tracking using a modified KLT tracker for a computer vision-based breast self-examination system , 2014, 2014 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM).

[13]  James H. Martin,et al.  Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition , 2000 .

[14]  A. Miller,et al.  Why is breast-cancer mortality declining? , 2003, The Lancet. Oncology.

[15]  R.N.G. Naguib,et al.  IRiS: an interactive reality system for breast self-examination training , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[16]  M. Plummer,et al.  International agency for research on cancer. , 2020, Archives of pathology.