ADD-Net: An Effective Deep Learning Model for Early Detection of Alzheimer Disease in MRI Scans

Mian Muhammad Sadiq Fareed, Shahid Zikria, Gulnaz Ahmed, Mui-zzud-din, Saqib Mahmood, Muhammad Aslam, Syeda Fizzah Jillani, Ahmad Moustafa, Muhammad Asad

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Alzheimer’s Disease (AD) is a neurological brain disorder marked by dementia and neurological dysfunction that affects memory, behavioral patterns, and reasoning. Alzheimer’s disease is an incurable disease that primarily affects people over 40. Alzheimer’s disease is diagnosed through a manual evaluation of a patient’s MRI scan and neuro-psychological examinations. Deep Learning (DL), a type of Artificial Intelligence (AI), has pioneered new approaches to automate medical image diagnosis. This study aims to create a reliable and efficient system for classifying AD using MRI by applying the deep Convolutional Neural Network (CNN). In this paper, we propose a new CNN architecture for detecting AD with relatively few parameters, and the proposed solution is ideal for training a smaller dataset. This proposed model successfully distinguishes the early stages of Alzheimer’s disease and shows class activation maps as a heat map on the brain. The proposed Alzheimer’s Disease Detection Network (ADD-Net) is built from scratch to precisely classify the stages of AD by decreasing parameters and calculation costs. The Kaggle MRI image dataset has a significant class imbalance problem, and we exploited a synthetic oversampling technique to evenly distribute the image among the classes to prevent the problem of class imbalance. The proposed ADD-Net is extensively evaluated against DenseNet169, VGG19, and InceptionResNet V2 using precision, recall, F1-score, Area Under the Curve (AUC), and loss. The ADD-Net achieved the following values for evaluation metrics: 98.63%, 99.76%, 98.61%, 98.63%, 98.58%, and 0.0549% accuracy, AUC, F1-score, precision, recall, and loss, respectively. The simulation results show that the proposed ADD-Net outperforms other state-of-the-art models in all the evaluation metrics.

Original languageEnglish
Pages (from-to)96930-96951
Number of pages22
JournalIEEE Access
Early online date05 Sept 2022
Publication statusPublished - 19 Sept 2022


  • Alzheimer's disease
  • Brain modeling
  • Class Activation
  • Computational modeling
  • Computer-Aided Diagnosis
  • Convolutional neural networks
  • Deep Learning
  • Deep learning
  • Diseases
  • Feature extraction
  • Image Classification
  • Imbalanced Data-set
  • Magnetic resonance imaging
  • MRI Data-set
  • Supervised Learning
  • Transfer Learning
  • transfer learning
  • image classification
  • supervised learning
  • computer-aided diagnosis
  • class activation
  • MRI data-set
  • imbalanced data-set


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