Breast cancer classification using parametric free thresholding adjacency statistics based Fibonacci patterns

Foram Sanghavi; Landry Kezebou; Karen Panetta; Sos Agaian

doi:10.1117/12.2558613

11 May 2020 Breast cancer classification using parametric free thresholding adjacency statistics based Fibonacci patterns

Foram Sanghavi, Landry Kezebou, Karen Panetta, Sos Agaian

Author Affiliations +

Proceedings Volume 11399, Mobile Multimedia/Image Processing, Security, and Applications 2020; 113990Q (2020) https://doi.org/10.1117/12.2558613
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

According to the American cancer society, the average risk of women getting diagnosed with breast cancer during their life is 13%. The World Health Organization also reports that the number of cancer cases is projected to rise to 19.3 million by 2025. Recent research works point out that physicians can only diagnose cancer with 79% accuracy while machine learning procedures achieve 91% accuracy or more. The current challenges are early cancer detection and the efficient and accurate diagnosis of histopathology tissue samples. Several Deep Learning breast cancer classification models have been developed to assist medical practitioners. However, these methods are data hungry and require thousands of training image samples, often coupled with data augmentation to achieve satisfactory results with long training hours. In this paper, we propose a machine learning classification model by integrating the Parameter free Thresholding Adjacency Statistics (PFTAS) with Fibonacci-p patterns for breast cancer detection. Computer simulations on BreakHis cancer datasets in comparison to other machine learning and deep learning-based methods show that (i) the presented method helps eliminate dependence on large training data and data augmentation, (ii) robustness to noise and background stains, and (iii) lightweight model easy to train and deploy.

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Foram Sanghavi, Landry Kezebou, Karen Panetta, and Sos Agaian "Breast cancer classification using parametric free thresholding adjacency statistics based Fibonacci patterns", Proc. SPIE 11399, Mobile Multimedia/Image Processing, Security, and Applications 2020, 113990Q (11 May 2020); https://doi.org/10.1117/12.2558613

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KEYWORDS

Cancer

Breast cancer

Data modeling

Binary data

Tumor growth modeling

Tissues

Machine learning

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