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A deep learning-based disease diagnosis with intrusion detection for a secured healthcare system

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Abstract

Security is considered the primary challenge in the healthcare industry in the aspect of the ethical and legal perspective of patient medical data. In existing approaches, the accessibility, reliability, and confidentiality of medical data are needed for more security in the healthcare industry. The main obstacles involved in the communication between the smartphone and monitoring devices allow a data theft attack. Hence, the secured model needs to be designed for providing data security in healthcare applications. By avoiding these challenges, this task provides secured intrusion detection with blockchain-based healthcare data transmission with the help of novel intelligence techniques. Initially, in the data acquisition phase, the gathered data from online sources consist of brain, skin, and retinal medical images. Then, the Weight Optimized Deep Belief Network (WO-DBN) checks the nodes to confirm whether the gathered data are in a malicious state or not at the time of transmission. The medical data are secured with the support of encryption and blockchain technology, where the medical images are enciphered by chaotic-map-aided image encryption through optimal key generation. These encrypted images are uploaded to the blockchain for secure data communication to the cloud server. Here, the authorized person can replace the data using the same optimal key and also it can be done after the decryption in the proposed chaotic map. The decrypted data are given to the final disease diagnosis phase for classifying the images without any information loss with the support of a Residual Network (Resnet101), where the final layer is restored by the “Deep Neural Network (DNN) and Long Short Term Memory (LSTM)” to enhance the classification accuracy and it is named Res-LSTM+DNN. The weight optimization in DBN, optimal key generation, and hyper-parameter tuning in classification are done by the Improved Dingo Optimizer (IDOX). From the overall result validation, the accuracy rate of the recommended approach scores 98.6%.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Rajalakshmi Institute of Technology, Chennai, India

    S. K. Rajesh Kanna

  2. Electronics and Communication Engineering, Vasireddy Venkatadri Institute of Technology, Guntur, India

    Mantripragada Yaswanth Bhanu Murthy

  3. Department of Infomation Technologies, SRES’s Sanjivani College of Engineering, Kopargaon, India

    Mahendra Bhatu Gawali

  4. IT Specialist at Opportunities Australia, Computer Networking, Victorian Institute of Technology, Melbourne, Australia

    Saleh Muhammad Rubai

  5. School of Management, Presidency University, Kolkata, India

    N. Srikanth Reddy

  6. Resbee Infomation Technologies Private Limited, Thuckalay, India

    G. Brammya & N. S. Ninu Preetha

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  1. S. K. Rajesh Kanna
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  2. Mantripragada Yaswanth Bhanu Murthy
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  3. Mahendra Bhatu Gawali
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  4. Saleh Muhammad Rubai
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All authors have made substantial contributions to conception and design, revising the manuscript, and the final approval of the version to be published. Also, all authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Correspondence to S. K. Rajesh Kanna.

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Kanna, S.K.R., Murthy, M.Y.B., Gawali, M.B. et al. A deep learning-based disease diagnosis with intrusion detection for a secured healthcare system. Knowl Inf Syst (2024). https://doi.org/10.1007/s10115-023-02030-1

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  • DOI: https://doi.org/10.1007/s10115-023-02030-1

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