An Explainable Model based on ResNet and Packet for Alzheimer’s Disease Diagnosis

Numerous studies have utilized machine learning and deep learning techniques for Alzheimer's disease diagnosis using Structural Magnetic Resonance Imaging (SMRI). However, some networks tend to underutilize the fine-grained spatial information across multiple layers, leading to incomplete utilization of the image's large-scale features. Additionally, overly complex networks increase the risk of overfitting early in the training process, resulting in suboptimal diagnostic accuracy. This paper introduces a novel deep-learning model designed for Alzheimer's disease diagnosis. The proposed model incorporates a Feature Extraction Layer (FEL) with three DeepSearch Units (DSUnits) and a Packet Layer to effectively capture detailed information. Inspired by the ResNet residual structure, the DSUnit employs a combination of convolutional layers with various kernel sizes and ReLU activation functions to extract multi-scale features from structural MRI images. The Packet Layer is composed of multiple feature sub-packets, which effectively reduce mutual interference between neurons, thereby enhancing diagnostic accuracy. The experiments are conducted on the ADNI dataset to compare the proposed model with EfficientNet, DenseNet, ResNet (18, 34, 50), AddFormer, MPS-FFA, and CDFA. The model achieved higher accuracy in binary classifications (CN vs. AD, CN vs. MCI) and exhibited competitive results in the multi-class classification (CN vs. MCI vs. AD) while using fewer parameters than AddFormer, MPS-FFA, and CDFA. Overall, the proposed method offers a more explainable and efficient approach to Alzheimer's diagnosis, demonstrating strong potential for clinical application. The code is available at https://github.com/ourWorks/PktNet and the data is acquired from https://adni.loni.usc.edu.