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Advance Convolutional Network Architecture for MRI Data Investigation for Alzheimer's Disease Early Diagnosis

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Abstract

DICOM, or digital imaging and communication in medicine, is a communication protocol that keeps a patient's medical information in a single file, as opposed to the name, date, and number of pixels contained in PNG or JPEG formats. DICOM photos undergo a number of preprocessing stages before being used, including transformation to HU, noise removal, tilt correction, cropping, and padding. After applying these preprocessing steps we will see the model accuracy got increased significantly. In this research, we flatten a comprehensive 3D model of the brain via coronal slices of the medial temporal lobe. Therefore, we have used various models of RESNET such as ResNet18, ResNet34, ResNet50, ResNet101 and ResNet152 to do a comparative analysis of the model accuracy to distinguish between moderate cognitive impairment, Alzheimer's disease, and cognitively normal. Machine learning and a slashing deep learning approach have outperformed traditional automation in the domain of computer vision at detecting subtle structures in intricate high-dimensional data spatially. Here, we have extracted the text keywords from the original image and convert to JPEG so we have both text and visual data. Image segmentation was then applied, and the resulting data were fed into several distinct ResNet deep learning networks, where the accuracy was evaluated using standard metrics, such as precision, recall, F1 score, and support.

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Data Availability

The data set generated and analyzed during the current investigation is available upon reasonable request from the corresponding author.

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Acknowledgements

The authors gratefully acknowledged the Galgotias University, Greater Noida, Uttar Pradesh and KPR Institute of Engineering and Technology, Coimbatore, Tamilnadu, India for providing the support and research facilities.

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No funding received for this research.

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Authors and Affiliations

  1. Galgotias University, Greater Noida, Uttar Pradesh, 203201, India

    Nilanjana Pradhan & Shraddha Sagar

  2. KPR Institute of Engineering and Technology, Coimbatore, Tamilnadu, India

    T. Jagadesh

Authors
  1. Nilanjana Pradhan
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  2. Shraddha Sagar
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  3. T. Jagadesh
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This collaborative research initiative was made achievable through the combined efforts and contributions of all participating authors.

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Correspondence to Nilanjana Pradhan.

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This article is part of the topical collection “Advances in Computational Approaches for Image Processing, Wireless Networks, Cloud Applications and Network Security” guest edited by P. Raviraj, Maode Ma and Roopashree H R.

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Pradhan, N., Sagar, S. & Jagadesh, T. Advance Convolutional Network Architecture for MRI Data Investigation for Alzheimer's Disease Early Diagnosis. SN COMPUT. SCI. 5, 167 (2024). https://doi.org/10.1007/s42979-023-02560-z

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