Medical image analysis using wavelet transform and deep belief networks

• Published in: Expert systems with applications 2017-11, Vol.86, p.190-198
• Main Authors: Khatami, Amin, Khosravi, Abbas, Nguyen, Thanh, Lim, Chee Peng, Nahavandi, Saeid
• Format: Article
• Language: English
• Subjects: Basis functions; Bayesian analysis; Belief networks; Breast; Classification; Classifiers; Computational efficiency; Deep belief network; Feature extraction; Image analysis; Image classification; Image management; Image processing; Kolmogorov Smirnov test; Machine learning; Medical imaging; Noise; Performance enhancement; Radial basis function; Radiography; Radiography image; Retrieval; Wavelet analysis; Wavelet transform; Wavelet transforms
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2017.05.073

•Propose a robust DBN-based classification model for X-ray images.•The wavelet transforms improves the deep classification performance.•The Kolmogorov Smirnov test is applied to find the most discriminative features.•An appropriately designed DBN selects features resulting in a fast classification. This paper introduces a three-step framework for classifying multiclass radiography images. The first step utilizes a de-noising technique based on wavelet transform (WT) and the statistical Kolmogorov Smirnov (KS) test to remove noise and insignificant features of the images. An unsupervised deep belief network (DBN) is designed for learning the unlabelled features in the second step. Although small-scale DBNs have demonstrated significant potential, the computational cost of training the restricted Boltzmann machine is a major issue when scaling to large networks. Moreover, noise in radiography images can cause a significant corruption of information that hinders the performance of DBNs. The combination of WT and KS test in the first step helps improve performance of DBNs. Discriminative feature subsets obtained in the first two steps serve as inputs into classifiers in the third step for evaluations. Five frequently used classifiers including naive Bayes, radial basis function network, random forest, sequential minimal optimization, and support vector machine and four different case studies are implemented for experiments using the Image Retrieval in Medical Application data set. The experimental results show that the three-step framework has significantly reduced computational cost and yielded a great performance for multiclass radiography image classification. Along with effective applications in image processing in other fields published in the literature, deep learning network in this paper has again demonstrated its robustness in handling a complex set of medical images. This implies that the proposed approach can be implemented in real practice for analysing noisy radiography images, which have many useful medical applications such as diagnosis of diseases related to lung, breast, musculoskeletal or pediatric studies.