Bayesian inference framework for bounded generalized Gaussian-based mixture model and its application to biomedical images classification

Biomedical image classification problem has attracted a lot of attention in medical engineering community and medicine applications. Accurate and automatic classification (eg, normal/abnormal or malignant/benign) has a variety of applications such as automatic decision making and is known to be very challenging. In this research, we address this problem by investigating the effectiveness of Bayesian inference methods for statistical bounded mixture models. Indeed, a novel approach termed as Bayesian learning for bounded generalized Gaussian mixture models is developed. The consideration of bounded mixture models is encouraged by their capability to take into account the nature of the data that is compactly supported. Furthermore, the consideration of Bayesian inference is more attractive compared to frequentist reasoning. In this work, we address main issues related to accurate data classification such as the effective estimation of the model's parameters and the selection of the optimal model complexity. Moreover, the problem of over- or under-fitting is treated by taking into account the uncertainty through introducing prior information about the model's parameters. A comparative study between different Gaussian-based models is also performed to evaluate the performance of the proposed framework. Experiments have been conducted on challenging biomedical image datasets that involve retinal images for diabetic retinopathy detection and mammograms for breast cancer detection. Obtained results are encouraging and show the benefits of our Bayesian framework.