TY - CHAP
T1 - An Intelligent Internet of Medical Things with Deep Learning Based Automated Breast Cancer Detection and Classification Model
AU - Mathapati, Mahantesh
AU - Chidambaranathan, S.
AU - Nasir, Abdul Wahid
AU - Vimalarani, G.
AU - Sheeba Rani, S.
AU - Gopalakrishnan, T.
N1 - Publisher Copyright:
© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - In recent decades, breast cancer (BC) is a significant cause of high mortality rate among women. The earlier identification of breast cancer helps to increase the survival rate by the use of appropriate medications. At the same time, internet of medical things (IoMT) and digital mammography finds helpful to diagnose breast cancer effectively in the beginning level itself. This paper presents an intelligent IoMT based breast cancer detection and diagnosis using deep learning model. IoMT based image acquisition process takes place to gather the digital mammogram images. The proposed model performs a set of processes namely preprocessing, K-means clustering based segmentation, local binary pattern (LBP) based feature extraction and deep neural network (DNN) based classification. The presented LBP-DNN model has the capability of effectively detecting and classifying breast cancer from mammogram images. The LBP-DNN model has been validated using MIAS database and an extensive comparative analysis is carried out to evaluate its performance. The experimental results ensured the superior performance of the LBP-DNN model with the maximum sensitivity of 71.64%, specificity of 75.87% and accuracy of 70.53%.
AB - In recent decades, breast cancer (BC) is a significant cause of high mortality rate among women. The earlier identification of breast cancer helps to increase the survival rate by the use of appropriate medications. At the same time, internet of medical things (IoMT) and digital mammography finds helpful to diagnose breast cancer effectively in the beginning level itself. This paper presents an intelligent IoMT based breast cancer detection and diagnosis using deep learning model. IoMT based image acquisition process takes place to gather the digital mammogram images. The proposed model performs a set of processes namely preprocessing, K-means clustering based segmentation, local binary pattern (LBP) based feature extraction and deep neural network (DNN) based classification. The presented LBP-DNN model has the capability of effectively detecting and classifying breast cancer from mammogram images. The LBP-DNN model has been validated using MIAS database and an extensive comparative analysis is carried out to evaluate its performance. The experimental results ensured the superior performance of the LBP-DNN model with the maximum sensitivity of 71.64%, specificity of 75.87% and accuracy of 70.53%.
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U2 - 10.1007/978-3-030-55833-8_11
DO - 10.1007/978-3-030-55833-8_11
M3 - Chapter
AN - SCOPUS:85093835594
T3 - Studies in Systems, Decision and Control
SP - 181
EP - 193
BT - Studies in Systems, Decision and Control
PB - Springer Science and Business Media Deutschland GmbH
ER -