Breast Tumor Classification with Enhanced Transfer Learning Features and Selection Using Chaotic Map-Based Optimization

Among women, breast cancer remains one of the most dominant cancer types. In the year 2022, around 2,87,800 new cases were diagnosed, and 43,200 women faced mortality due to this disease. Analysis and processing of mammogram images is vital for its earlier identification and thus helps in reducing m...

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Bibliographic Details
Published in:International journal of computational intelligence systems 2024-02, Vol.17 (1), p.1-18, Article 18
Main Authors: Chakravarthy, Sannasi, Nagarajan, Bharanidharan, Kumar, V. Vinoth, Mahesh, T. R., Sivakami, R., Annand, Jonnakuti Rajkumar
Format: Article
Language:English
Subjects:
Artificial Intelligence
Breast cancer
Computational Intelligence
Control
Crow search algorithm
Deep-learning
Engineering
Mammograms
Mammography
Mathematical Logic and Foundations
Mechatronics
Optimization
Research Article
Robotics
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Summary:Among women, breast cancer remains one of the most dominant cancer types. In the year 2022, around 2,87,800 new cases were diagnosed, and 43,200 women faced mortality due to this disease. Analysis and processing of mammogram images is vital for its earlier identification and thus helps in reducing mortality rates and facilitating effective treatment for women. Accordingly, several deep-learning techniques have emerged for mammogram classification. However, it is still challenging and requires promising solutions. This study proposed a newer automated computer-aided implementation for breast cancer classification. The work starts with enhancing the mammogram contrast using a haze-reduced adaptive technique followed by augmentation. Afterward, EfficientNet-B4 pre-trained architecture is trained for both original and enhanced sets of mammograms individually using static hyperparameters’ initialization. This provides an output of 1792 feature vectors for each set and then fused using a serial mid-value-based approach. The final feature vectors are then optimized using a chaotic-crow-search optimization algorithm. Finally, the obtained significant feature vectors were classified with the aid of machine learning algorithms. The evaluation is made using INbreast and CBIS-DDSM databases. The proposed framework attained a balanced computation time with a maximum classification performance of 98.459 and 96.175% accuracies on INbreast and CBIS-DDSM databases, respectively.
ISSN:1875-6883
1875-6883
DOI:10.1007/s44196-024-00409-8