Optimization enabled ResNet features with transfer learning for Alzheimer's disease detection

Millions of individuals worldwide suffer from Alzheimer's Disease (AD), a debilitating degenerative condition. Early detection of Alzheimer's disease is critical to ensure effective treatment and better patient outcomes. In the past few years, advanced medical imaging techniques, particularly MRI, have shown potential for diagnosing Alzheimer's disease. However, developing accurate and efficient techniques for Alzheimer's disease detection offcuts a demanding duty suitable to the complication of medical images and the limited availability of labelled data. The early detection of Alzheimer's disease is critical for effective treatment and management of this debilitating neurodegenerative condition. The research proposes a novel method for Alzheimer's disease detection using an optimization-enabled ResNet feature extraction technique with transfer learning that is proposed by combining LeNet and VGG networks. The pre-processing was done using image resizing and median filter and the featureextraction was conducted using the proposed Walrus Optimization Algorithm-Residual neural network (WOA-ResNet), where WOA is employed for training ResNet. The conducted experiments with the Alzheimer's dataset achieved a higher accuracy using the proposed LeNet-VGG method. The findings suggest that optimization-enabled ResNet feature extraction with LeNet-VGG networks can significantly improve the accuracy of Alzheimer's disease detection. The presented method achieved maximum accuracy value of 95.37 %, sensitivity value of 97.24 % and specificity value of 93.73 %.