Parametric Image-based Breast Tumor Classification Using Convolutional Neural Network in the Contourlet Transform Domain

Kabir, Shahriar Mahmud; Shihavuddin, Asm; Tanveer, Md. Sayed; Bhuiyan, Mohammed Imamul Hassan

doi:10.1109/icece51571.2020.9393091

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…As a CNN would require all the images to have the same sizes, all the images were resized to 224 × 224, and then, the corresponding six sub-band images were stacked together to form 6000 3D stack images of size 224 × 224 × 6. The CNN network employed for this work is a modified version of the custom CNN network provided in [41]; the differences between that network and the proposed network have an input of 224 × 224 × 6 3D image stack, and the features extracted from the outermost layer (the Global Average Pooling layer) were provided to seven different classifiers. The inspiration for not using a pre-trained network for the WCP images is, as we claimed, that the pre-trained networks were built for 3-channel visual images with spatial dimensions, and, they were not compatible with our 3D stack of transform domain coefficient images.…”

Section: Proposed Classification Schemesmentioning

confidence: 99%

RiIG Modeled WCP Image-Based CNN Architecture and Feature-Based Approach in Breast Tumor Classification from B-Mode Ultrasound

et al. 2021

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This study presents two new approaches based on Weighted Contourlet Parametric (WCP) images for the classification of breast tumors from B-mode ultrasound images. The Rician Inverse Gaussian (RiIG) distribution is considered for modeling the statistics of ultrasound images in the Contourlet transform domain. The WCP images are obtained by weighting the RiIG modeled Contourlet sub-band coefficient images. In the feature-based approach, various geometrical, statistical, and texture features are shown to have low ANOVA p-value, thus indicating a good capacity for class discrimination. Using three publicly available datasets (Mendeley, UDIAT, and BUSI), it is shown that the classical feature-based approach can yield more than 97% accuracy across the datasets for breast tumor classification using WCP images while the custom-made convolutional neural network (CNN) can deliver more than 98% accuracy, sensitivity, specificity, NPV, and PPV values utilizing the same WCP images. Both methods provide superior classification performance, better than those of several existing techniques on the same datasets.

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Section: Proposed Classification Schemesmentioning

confidence: 99%