Abstract
Invasive ductal carcinoma (IDC) is the type of breast cancer that occurs most frequently. IDC detection is a laborious process and important task. Furthermore, accurate IDC detection is necessary to provide patients with appropriate medical care. Deep learning technique is far more reliable in these kinds of medical scenarios. This study proposes a deep learning model for identifying IDC in breast cancer histopathology images as a tumor is malignant or benign. Residual Network (ResNet) is a component of our deep learning model. Extract the similar features and train to identify future images; this residual network is given tagged BH image data. As a result, it can recognize the presence of IDC in images of breast tissue lesions. The proposed model produces higher accuracy than the existing models.
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https://www.kaggle.com/datasets/paultimothymooney/breast-histopathology-images
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Aditya, K.V., Leelavathy, N., Sujatha, B., Tamilkodi, R., Sattibabu, D. (2023). Deep Learning Framework for the Detection of Invasive Ductal Carcinoma. In: Bhateja, V., Carroll, F., Tavares, J.M.R.S., Sengar, S.S., Peer, P. (eds) Intelligent Data Engineering and Analytics. FICTA 2023. Smart Innovation, Systems and Technologies, vol 371. Springer, Singapore. https://doi.org/10.1007/978-981-99-6706-3_26
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DOI: https://doi.org/10.1007/978-981-99-6706-3_26
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