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Medical image analysis using wavelet transform and deep belief networks
journal contribution
posted on 2017-11-01, 00:00 authored by Seyedamin Khatami, Abbas KhosraviAbbas Khosravi, Thanh Thi NguyenThanh Thi Nguyen, Chee Peng LimChee Peng Lim, Saeid NahavandiThis 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.
History
Journal
Expert systems with applicationsVolume
86Pagination
190 - 198Publisher
ElsevierLocation
Amsterdam, The NetherlandsPublisher DOI
ISSN
0957-4174Language
EngPublication classification
C1.1 Refereed article in a scholarly journalCopyright notice
2017, ElsevierUsage metrics
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No categories selectedKeywords
Deep belief networkScience & TechnologyTechnologyComputer Science, Artificial IntelligenceEngineering, Electrical & ElectronicOperations Research & Management ScienceComputer ScienceEngineeringWavelet transformRadiography imageFeature extractionKolmogorov Smirnov testClassificationCLASSIFICATION-SYSTEMFEATURE-SELECTIONRETRIEVALIDENTIFICATIONINTELLIGENCEINFORMATIONDIAGNOSISFRAMEWORKBAYES
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