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%. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.