DETECTION OF ALZHEIMER’S DISEASE USING CNN
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Abstract: Treatment is complicated by the laborious and prone to error nature of modern MRI analysis for brain tumour detection. Using several MRI datasets, we suggest a technique that combines deep learning, conventional classifiers, and CNNs. Algorithms for activation and SVM validation are used. Our CNN surpassed prior records with an accuracy of 99.74% when it was implemented in Python using TensorFlow and Keras. With this automated method, brain tumour diagnosis should be greatly improved, allowing patients' treatment choices to be made more quickly.
Keywords: Alzheimer's, CNN, Keras, TensorFlow, Max Pooling, Batch Normalization. Downloads: | DOI: 10.17148/IARJSET.2024.11642 How to Cite: [1] Jeevitha H, Kavya R, Sushma K Sattigeri, "DETECTION OF ALZHEIMER'S DISEASE USING CNN," International Advanced Research Journal in Science, Engineering and Technology (IARJSET), DOI: 10.17148/IARJSET.2024.11642 Copy Citation Call for Papers Rapid Publication 24/7 April 2026 Submission: eMail paper now Notification: Immediate Publication: Immediately with eCertificates Frequency: Monthly Downloads Paper Format Copyright Form
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[1] Jeevitha H, Kavya R, Sushma K Sattigeri, “DETECTION OF ALZHEIMER’S DISEASE USING CNN,” International Advanced Research Journal in Science, Engineering and Technology (IARJSET), DOI: 10.17148/IARJSET.2024.11642
