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In this work, we design a multi-scale convolutional neural networks (MCNNs) model for the previous detection of Diabetes mellitus. The detection technique is based on the proposed model's training by analyzing the data taken from non-diabetic and diabetic patients from PIMA, an Indian database. To achieve a high level of accurate training data for diabetes detection, we finally detect the parameters like diastolic blood pressure, body mass index (BMX), triceps skin thickness, number of pregnancies, inheritance and age factor. Based on CNN acting at different resolutions, the proposed architecture avoids the traditional step of manual extraction of features by extracting features and classifying them at one time within the same network of data neurons. The proposed approach provides better classification results than the usual methods for safer diagnosis of Diabetes.
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