A Custom Convolutional Neural Network Model-Based Bioimaging Technique for Enhanced Accuracy of Alzheimer’s Disease Detection

Alzheimer’s disease (AD), an intense neurological illness, severely impacts memory, behavior, and personality, posing a growing concern worldwide due to the aging population. Early and accurate detection is crucial as it enables preventive measures. However, current diagnostic methods are often inaccurate in identifying the disease in its early stages. Although deep learning-based bioimaging has shown promising results in medical image classification, challenges remain in achieving the highest accuracy for detecting AD. Existing approaches, such as ResNet50, VGG19, InceptionV3, and AlexNet have shown potential, but they often lack reliability and accuracy due to several issues. To address these gaps, this paper suggests a novel bioimaging technique by developing a custom Convolutional Neural Network (CNN) model for detecting AD. This model is designed with optimized layers to enhance feature extraction from medical images. The experiment’s first phase involved the construction of the custom CNN structure with three max-pooling layers, three convolutional layers, two dense layers, and one flattened layer. The Adam optimizer and categorical cross-entropy were adopted to compile the model. The model’s training was carried out on 100 epochs with the patience set to 10 epochs. The second phase involved augmentation of the dataset images and adding a dropout layer to the custom CNN model. Moreover, fine-tuned hyperparameters and advanced regularization methods were integrated to prevent overfitting. A comparative analysis of the proposed model with conventional models was performed on the dataset both before and after the data augmentation. The results validate that the proposed custom CNN model significantly overtakes pre-existing models, achieving the highest validation accuracy of 99.53% after data augmentation while maintaining the lowest validation loss of 0.0238. Its precision, recall, and F1 score remained consistently high across all classes, with perfect scores for the Moderate Demented and Non-Demented categories after augmentation, indicating superior classification capability.