Abstract
The research on cancer is taken a superior space for medical and technological professionals. Since a few years its research grows for both where maximum involvement of technological research is clearly visible. In the article, we analyze the earlier works for detection. Along with it, a model is proposed that performs well and will take the readers to next level of work. A concatenated model containing convolutional layers and long short term memory layers is proposed for cancer detection from the histopathological images. The Adam optimization algorithm is used for minimizing the error and to train the network that is one of the supervised learning methods. To check the practicability of the proposed method publicly available breast cancer dataset is taken to train, validate, and test the network. The proposed method resulted in 95.32% testing accuracy.
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Das, A., Mohapatra, S.K., Mohanty, M.N. (2023). Breast Cancer Detection Using Concatenated Deep Learning Model. In: Swarnkar, T., Patnaik, S., Mitra, P., Misra, S., Mishra, M. (eds) Ambient Intelligence in Health Care. Smart Innovation, Systems and Technologies, vol 317. Springer, Singapore. https://doi.org/10.1007/978-981-19-6068-0_10
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DOI: https://doi.org/10.1007/978-981-19-6068-0_10
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