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Alzheimer’s disease (AD) is a global disease that is in a serious and incurable stage. New approaches to AD diagnosis include a user-friendly platform for patient appointments and help doctors upload photos and medications accurately. Diagnosis and treatment of AD is a complex neurodegenerative disease that requires early diagnosis and appropriate treatment. The proposed system addresses this challenge by providing a seamless process for patients to request an appointment, eliminating the need for coordination, and reducing wait times. In this work, use the ResNet algorithm and MobileNet algorithm. The results of this model improve doctors’ ability to diagnose patients more effectively. Doctors can also send medical notes for personalized care. The system aims to increase the efficiency and effectiveness of Alzheimer’s diagnosis using technology. This approach holds great promise for using deep learning to advance AD classification, provide patients with timely access to treatment, and support clinicians in delivering optimal care.

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Alzheimer’s Disease Multi-task Classification Using ResNet, MobileNet

  • 会议论文

  • 发表日期：

    2025年

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  • Addagulla Mahalaxmi,
  • Mynampati Meghana Reddy,
  • Kandibanda Pramitha,
  • Ch. Vijayabhaskar,
  • V. Kakulapati

摘要

Alzheimer’s disease (AD) is a global disease that is in a serious and incurable stage. New approaches to AD diagnosis include a user-friendly platform for patient appointments and help doctors upload photos and medications accurately. Diagnosis and treatment of AD is a complex neurodegenerative disease that requires early diagnosis and appropriate treatment. The proposed system addresses this challenge by providing a seamless process for patients to request an appointment, eliminating the need for coordination, and reducing wait times. In this work, use the ResNet algorithm and MobileNet algorithm. The results of this model improve doctors’ ability to diagnose patients more effectively. Doctors can also send medical notes for personalized care. The system aims to increase the efficiency and effectiveness of Alzheimer’s diagnosis using technology. This approach holds great promise for using deep learning to advance AD classification, provide patients with timely access to treatment, and support clinicians in delivering optimal care.