Abstract
Early identification of breast cancer is crucial to increase the odds of a positive therapy, thus contributing decrease in mortality rate. Screen-based mammography and digitized mammography are two methods of mammography, out of which digitized mammography one is more secure. The proposed approach is based on ResNet-50 and Bi-LSTM network with extreme gradient boost (XG-boost), which resolves the problem of vanishing and exploding gradient, and contains the memory units in bi-direction making predictions easier than the existing approaches and provides an accuracy of 97.17% on the test set which is much higher than the accuracy of the existing approaches. It is vital to note that feature engineering is critical in this industry. Due to long short-term memory network (LSTM) economical memory, this research attempts to extract useful features using attention layers and to decrease error using the Bi-LSTM technique.
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Chhabra, A., Bharti, M.R. (2023). Mammography Image Classification and Detection by Bi-LSTM with Residual Network Using XG-Boost Approach. In: Bhateja, V., Yang, XS., Chun-Wei Lin, J., Das, R. (eds) Intelligent Data Engineering and Analytics. FICTA 2022. Smart Innovation, Systems and Technologies, vol 327. Springer, Singapore. https://doi.org/10.1007/978-981-19-7524-0_15
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DOI: https://doi.org/10.1007/978-981-19-7524-0_15
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