The use of the multiscale generalized radial basis function (MSRBF) neural networks for image feature extraction and medical image analysis and classification is proposed for the first time in this work. The MSRBF networks hold a simple and flexible architecture that has been successfully used in forecasting and model structure detection of input-output nonlinear systems. In this work instead, MSRBF networks are part of an integrated computer-aided diagnosis (CAD) framework for breast cancer detection, which holds three stages: an input-output model is obtained from the image, followed by a high-level image feature extraction from the model and a classification module aimed at predicting breast cancer. In the first stage, the image data is rendered into a multiple-input-single-output (MISO) system. In order to improve the characterisation, the nonlinear autoregressive with exogenous inputs (NARX) model is introduced to rearrange the available input-output data in a nonlinear way. The forward regression orthogonal least squares (FROLS) algorithm is then used to take advantage of the previous arrangement by solving the system as a model structure detection problem and finding the output layer weights of the NARX-MSRBF network. In the second stage, once the network model is available, the feature extraction takes place by stimulating the input to produce output signals to be compressed by the discrete cosine transform (DCT). In the third stage, we leverage the extracted features by using a clustering algorithm for classification to integrate a CAD system for breast cancer detection. To test the method performance, three different and well-known public image repositories were used: the mini-MIAS and the MMSD for mammography, and the BreaKHis for histopathology images. A comparison exercise was also made between different database partitions to understand the mammogram breast density effect in the performance since there are few remarks in the literature on this factor. Classification results show that the new CAD method reached an accuracy of 93.5% in mini-Mammo graphic image analysis society (mini-MIAS), 93.99% in digital database for screening mammography (DDSM) and 86.7% in the BreaKHis. We found that the MSRBF networks are able to build tailored and precise image models and, combined with the DCT, to extract high-quality features from both black and white and coloured images.
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The authors acknowledge the financial support to Carlos Beltran-Perez from the Mexican National Council of Science and Technology (CONACYT). The authors gratefully acknowledge that part of the work was supported by the Engineering and Physical Sciences Research Council (EPSRC) under grant EP/I011056/1 and platform grant EP/H00453X/1.
Carlos Beltran-Perez received the B. Eng. degree in systems engineering from the Autonomous Metropolitan University, Mexico, and received M. Sc. degree in systems engineering from the Autonomous University of Nuevo Leon, Mexico. He received the Ph. D. degree in automatic control and systems engineering from University of Sheffield, UK in 2019. He is currently a lecturer at Departments of Industrial Engineering and Computer Engineering, Monterrey Institute of Technology and Higer Education, Campus Toluca, Mexico, and a graduate researcher at University of Sheffield, UK.
His research interests include identification and modelling for complex systems, medical image processing, NARMAX methodologies, multilayer neural networks, forecasting and time series analysis.
Hua-Liang Wei received the Ph. D. degree in signal processing and complex system from Department of Automatic Control and Systems Engineering, University of Sheffield, UK in 2004. He is currently a senior lecturer in Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK.
His research interests include system identification and data analytics for complex systems, data driven modeling and data mining, NARMAX methodology and its applications, statistical digital signal processing, machine learning and neural networks, spatio-temporal system modeling, neuro-wavelet models for learning, nonstationary (time varying) process modeling, forecasting of complex dynamic processes, generalized regression analysis, linear and nonlinear optimization, and multidisciplinary applications in medicine and bio-medicine, medical informatics, space weather and environmental sciences.
Adrian Rubio-Solis received the B. Eng. degree in robotics and the M. Sc degree in electrical engineering from the the National Polytechnic Institute of Mexico, Mexico in 2003 and 2005 respectively. He received the Ph. D. degree in automatic control and systems engineering from the University of Sheffield, UK in 2014. He is currently working as research fellow at the Submarine Robotics Laboratory, CIDESI Queretaro, Mexico.
His research interests include data-driven modelling, image processing, fuzzy control theory and machine learning applied to submarine robotics.
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Beltran-Perez, C., Wei, HL. & Rubio-Solis, A. Generalized Multiscale RBF Networks and the DCT for Breast Cancer Detection. Int. J. Autom. Comput. 17, 55–70 (2020). https://doi.org/10.1007/s11633-019-1210-y
- Nonlinear system identification
- image processing
- discrete cosine transform
- radial basis functions
- computer-aided diagnosis
- neural networks