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Deep feature extraction and classification of breast ultrasound images

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Abstract

Controlled despeckling (structure/edges/feature preservation with smoothing the homogeneous areas) is a desired pre-processing step for the design of computer-aided diagnostic (CAD) systems using ultrasound images as the presence of speckle noise masks diagnostically important information making interpretation difficult even for experienced radiologist. For efficiently classifying the breast tumors, the conventional CAD system designs use hand-crafted features. However, these features are not robust to the variations in size, shape and orientation of the tumors resulting in lower sensitivity. Thus deep feature extraction and classification of breast ultrasound images have recently gained attention from research community. The deep networks come with an advantage of directly learning the representative features from the images. However, these networks are difficult to train from scratch if the representative training data is small in size. Therefore transfer learning approach for deep feature extraction and classification of medical images has been widely used. In the present work the performance of four pre-trained convolutional neural networks VGG-19, SqueezeNet, ResNet-18 and GoogLeNet has been evaluated for differentiating between benign and malignant tumor types. From the results of the experiments, it is noted that CAD system design using GoogLeNet architecture for deep feature extraction followed by correlation based feature selection and fuzzy feature selection using ANFC-LH yields highest accuracy of 98.0% with individual class accuracy value of 100% and 96% for benign and malignant classes respectively. For differentiating between the breast tumors, the proposed CAD system design can be utilized in routine clinical environment.

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Acknowledgments

The authors would like to thank Dr. Shruti Thakur, Kamla Nehru Hospital, Shimla for grading the filtered images and marking the tumor contours in the original ultrasound images. The authors would also like to thank Director, Thapar Institute of Engineering and Technology, Patiala and Director, CSIR-CSIO, Chandigarh for constant patronage and support in carrying out the present research.

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  1. Thapar Institute of Engineering and Technology, Patiala, Punjab, 147004, India

    Kriti & Ravinder Agarwal

  2. CSIR-Central Scientific Instruments Organisation (CSIR-CSIO), Chandigarh, 160030, India

    Jitendra Virmani

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Kriti, Virmani, J. & Agarwal, R. Deep feature extraction and classification of breast ultrasound images. Multimed Tools Appl 79, 27257–27292 (2020). https://doi.org/10.1007/s11042-020-09337-z

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