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Bayesian optimized novel CNN for improved diagnosis from ultrasound breast tumor images

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Abstract

Convolutional neural networks (CNNs) have played a significant role in feature extraction and tasks thereafter for accurate and automated diagnosis from ultrasound (US) breast tumor images. However, using pre-trained architectures and transfer learning for feature extraction could cause negative transfer in medical domain. Also publicly accessible online training/ validation US breast tumor datasets are seldom available. Hence, it becomes prudent to develop alternate CNN architectures as feature extraction backbones which are trained on smaller datasets without any consequences of overfitting. In this paper, a CNN was developed for feature extraction and prediction of breast tumor as benign/ malignant, with hyper parameters (learning rate, regularization factor, momentum, section depth and number of convolution filters) optimized using bayesian optimization. To further prevent any overfitting due to limited training data, a novel neutrosophic augmentation method was also introduced. The obtained simulation results on three different test datasets show that the classification accuracy of the optimized CNN outperforms by at least 3% than many other state-of-the-art deep architectures and significantly greater than 5% for shallow architectures. For segmenting the tumor region, the convolution maps from the higher layers of the optimized CNN are clustered to provide the initial contour for segmentation using active contours. The segmentation metrics with respect to ground truth is greater for the proposed work when compared with U-Net and fully Convolutional Network based segmentation. The structural similarity and mean segmentation error for the proposed method and the latter cases are 0.98, 0.93 and 0.92, and 0.01, 0.21 and 0.28 respectively.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Mar Baselios College of Engineering and Technology, Mar Ivanios Vidyanagar, Nalanchira, Trivandrum, Kerala, 695015, India

    Revathy Sivanandan & Jayakumari J

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  1. Revathy Sivanandan
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  2. Jayakumari J
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Contributions

Revathy Sivanandan: Methodology, Software, Writing-Original draft preparation, Visualization, Investigation, Validation.

Jayakumari J: Supervision, Validation, Writing- Reviewing and Editing.

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Correspondence to Revathy Sivanandan.

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Sivanandan, R., J, J. Bayesian optimized novel CNN for improved diagnosis from ultrasound breast tumor images. Multimed Tools Appl 82, 22815–22833 (2023). https://doi.org/10.1007/s11042-023-14468-0

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  • DOI: https://doi.org/10.1007/s11042-023-14468-0

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