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Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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Abstract

Aim

The automatic computer-assisted mammogram classification system is important for women patients to detect and diagnose the cancer regions. In this work, the mammogram images are classified into three cases: healthy, benign and cancer, using the proposed Resource Efficient Convolutional Neural Network (RECNN architecture).

Methods

The proposed mammogram image classification system consists of Data Augmentation (DA) module and Spatial transformation module and CNN architecture with a segmentation module. The DA methods are used to increase the mammogram image count and Spatial Gabor Transform is used as the spatial transformation module for transforming the spatial pixels into spatial-frequency pixels. Then, the proposed RECNN architecture is used to perform the classification of mammogram images into healthy, benign and cancer cases. Further, the cancer mammogram images are diagnosed as either ‘Early’ or ‘Severe’ using the proposed RECNN architecture in this work.

Results

The proposed MCDS obtains 98.65% SeDR, 98.93% SpDR and 98.84% ADR for benign case mammogram images on DDSM dataset and also obtains 98.84% SeDR, 98.7% SpDR and 98.92% ADR for cancer case mammogram images on DDSM dataset. The proposed MCDS obtains 98.94% SeDR, 98.86% SpDR and 98.96% ADR for benign case mammogram images on MIAS dataset and also obtains 98.89% SeDR, 98.88% SpDR and 99.03% ADR for cancer case mammogram images on MIAS dataset.

Conclusion

This proposed method is tested on the mammogram images from DDSM and MIAS datasets and the experimental results are compared with other similar mammogram classification works in this paper. Based on several performance evaluation measures, the experimental results show that MCDS outperforms the state-of-the-art methods currently used for the diagnosis and detection of mammography cancer.

© 2024 The Author(s). Published by Bentham Science Publisher.
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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/content/journals/cmir/10.2174/0115734056309483240805111535
2024-01-01
2025-07-09

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/content/journals/cmir/10.2174/0115734056309483240805111535
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Computer-aided Detection and Diagnosis of Cancer Regions in Mammogram Images using Resource-Efficient CNN Architecture
Curr. Med. Imaging 20, e15734056309483 (2024); https://doi.org/10.2174/0115734056309483240805111535
/content/journals/cmir/10.2174/0115734056309483240805111535
/content/journals/cmir/10.2174/0115734056309483240805111535
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  • Article Type:     Research Article
Keyword(s): Benign; Cancer; Datasets; Mammogram; Resource Efficient Convolutional Neural Network (RECNN architecture); Spatial

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