Abstract
Breast cancer is the second major reason of cancer death among women. Automatic and accurate detection of cancer at an early stage, allow proper treatment to the patients and drastically reduces the death rate. Usually, the performance of conventional deep learning networks degrades in small databases due to a lack of data for proper training. In this work, a deep learning-based breast cancer detection framework is suggested in which EfficientNet is employed to deliver excellent performance even in small databases. The uniform and adaptive scaling of depth, width, and resolution result in an efficient detection framework by maintaining a proper tradeoff between classification performance and computational cost. Moreover, an Laplacian of Gaussian-based modified high boosting (LoGMHB) is applied in addition to data augmentation as a preprocessing step prior to the deep learning model to boost performance further. Here, performance analysis is conducted in both mammogram and ultrasound modalities to display the proposed method’s superiority. The experimental results with five-fold cross-validation show that the proposed breast cancer scheme outperforms other comparison methods in all the performance measures.
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Acknowledgement
This research has been partly financially supported by The Analytical Center for the Government of the Russian Federation (Agreement No. 70-2021-00143 dd. 01.11.2021, IGK 000000D730321P5Q0002). Authors acknowledge the technical support and review feedback from AILSIA symposium held in conjunction with the 22nd International Conference on Intelligent Systems Design and Applications (ISDA 2022).
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Sahu, A., Das, P.K., Meher, S., Panda, R., Abraham, A. (2023). An Efficient Deep Learning-Based Breast Cancer Detection Scheme with Small Datasets. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 717. Springer, Cham. https://doi.org/10.1007/978-3-031-35510-3_5
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