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An efficient deep neural network based abnormality detection and multi-class breast tumor classification

  • 1176: Artificial Intelligence and Deep Learning for Biomedical Applications
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Abstract

Breast tumor is one of the major cause of death among women all over the world. Ultrasound imaging-based breast abnormality detection and classification play a vital role to develop an automatic computer-aided diagnostic system. In this paper, deep learning technology is integrated with ultrasound images for pre-screening of breast cancer. Two breast ultrasound image datasets are trained on different deep-learning architectures with image augmentation. Convolutional neural network extracts the features from training ultrasound images which are fine-tuned for multiple iterations. The experimental outcomes indicate accurate and rapid prediction performance on the test dataset of 2D B-mode ultrasound images, signifying a promising approach for assistance to radiologists in clinical applications with the use of deep learning. Results demonstrate the proposed method attains an accuracy, sensitivity, and specificity of 96.31%, 92.63%, and 96.71% respectively. About 12 B-mode 2D ultrasound image frames can be processed per second which marks it as a highly efficient system. The proposed method gives better performance compared to other methods which shows its effectiveness in real-time computer-aided diagnosis of breast tumor and benign-malignant classification.

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Acknowledgements

This research was supported in parts by the Grants from Department of Science and Technology, Government of India, grant number SEED/TIDE/2018/6/G.

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Authors and Affiliations

  1. Centre for Advanced Studies, Dr. A.P.J.Abdul Kalam Technical University, Lucknow, India

    Rakesh Chandra Joshi, Divyanshu Singh, Vaibhav Tiwari & Malay Kishore Dutta

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  1. Rakesh Chandra Joshi
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  2. Divyanshu Singh
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  4. Malay Kishore Dutta
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Contributions

RCJ, DS and MKD designed the study. RCJ, DS and VT performed data analysis under supervision of MKD. RCJ, DS, VT and MKD drafted the paper. All authors approved the final version of the draft.

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Correspondence to Malay Kishore Dutta.

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Joshi, R.C., Singh, D., Tiwari, V. et al. An efficient deep neural network based abnormality detection and multi-class breast tumor classification. Multimed Tools Appl 81, 13691–13711 (2022). https://doi.org/10.1007/s11042-021-11240-0

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