Local Attention-Based Descriptor Definition using Vision Transformer for Breast Cancer Identification

Local Attention-Based Descriptor Definition using Vision Transformer for Breast Cancer Identification

© 2022 by IJETT Journal
Volume-70 Issue-12
Year of Publication : 2022
Author : Anish Anurag, Aniket Das, Jaya H. Dewan, Rik Das, Govind Kumar Jha, Sudeep D. Thepade
DOI : 10.14445/22315381/IJETT-V70I12P230

How to Cite?

Anish Anurag, Aniket Das, Jaya H. Dewan, Rik Das, Govind Kumar Jha, Sudeep D. Thepade, "Local Attention-Based Descriptor Definition using Vision Transformer for Breast Cancer Identification," International Journal of Engineering Trends and Technology, vol. 70, no. 12, pp. 317-327, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I12P230

Assorted morphological characteristics of breast cancer have categorized it as a heterogeneous life-threatening ailment for females. Researchers have proposed automated approaches for detecting malignant breast cancer from patient images for faster diagnosis with higher precision. Descriptor definition with local attention by dividing a given image into patches can result in a robust representation of the breast cancer histopathological images for identification of malignancy. This work has experimented with feature extraction with local attention-based vision transformers and has evaluated the classification results under multiple classification environments. The research paper has also compared the resilience of classical descriptors generated using handcrafted methods and automated pre-trained convolutional neural network (CNN)- based feature extraction. Three separate handcrafted feature extraction methods, namely Color Histogram, Local Binary Pattern (LBP), and Oriented FAST and Rotated BRIEF(ORB), are used in the process, along with pre-trained CNN-based feature extraction methods (InceptionNet-v1, EfficientNet-B7, and ResNet-50). The experimentation is performed using the BreakHis dataset, and the results have revealed the superior performance of vision transformer-based features as compared to all other individual features considered. Furthermore, early fusion-based descriptors with different combinations of handcrafted and deep-learning features are created to investigate any improvement in the generalization of descriptors. The results have indicated that the feature extracted with local attention-based vision transformer overfits with early fusion has the best performance when evaluated individually in three different classification environments

Breast cancer, Local attention, Vision transformer, Color histogram, Local Binary Pattern, Convolutional Neural Networks.

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