Dual Attention Aware Octave Convolution Network for Early-Stage Alzheimer's Disease Detection


  • Banupriya Rangaraju Department of Computer Science and Engineering, K. S. R College of Engineering, Tiruchengode, 637215, India.
  • Thilagavathi Chinnadurai Department of Infomation Technology, M. Kumarasamy College of Engineering, Karur, 639113, India.
  • Sarmiladevi Natarajan Department of Artificial Intelligence and Data Science, Kongunadu College of Engineering and Technology, Trichy, 621215, India.
  • Vishnu Raja Department of Computer Science and Engineering, Kongu Engineering College, Erode, 638060, India.




Alzheimer's disease, Brain disorder, deep learning, depth-wise separable convolution, spatial attention blocks


Some of the most fundamental human capabilities, including thought, speech, and movement, may be lost due to brain illnesses. The most prevalent form of dementia, Alzheimer's disease (AD), is caused by a steady decline in brain function and is now incurable. Despite the challenges associated with making a conclusive diagnosis of AD, the field has generally shifted toward making diagnoses justified by patient records and neurological analysis, such as MRI. Reports of studies utilizing machine learning for AD identification have increased in recent years. In this publication, we report the results of our most recent research. It details a deep learning-based, 3D brain MRI-based method for automated AD detection. As a result, deep learning models have become increasingly popular in recent years for analyzing medical images. To aid in detecting Alzheimer's disease at an initial phase, we suggest a dual attention-aware Octave convolution-based deep learning network (DACN). The three main parts of DACN are as follows: First, we use Patch Convolutional Neural Network (PCNN) to identify discriminative features within each MRI patch while simultaneously boosting the features of abnormally altered micro-structures in the brain; second, we use an Octave convolution to minimize the spatial redundancy and widen the field of perception of the brain's structure; and third, we use a dual attention aware convolution classifier to dissect the resulting depiction further. An outstanding test accuracy of 99.87% is reached for categorizing dementia phases by employing the suggested method in experiments on a publically available ADNI (Alzheimer's Disease Neuroimaging Initiative) dataset. The proposed model was more effective, efficient, and reliable than the state-of-the-art models through our comparisons.