Advanced AI-Driven Framework for Comprehensive Dermatological Image Analysis

Abstract

Prompt and precise diagnosis of Alzheimer's disease is essential for optimal patient control, particularly given the progressive and degenerative characteristics of this neurological condition. A complete framework has been established for the category and detection of Alzheimer's ailment severity by means of utilizing annotated magnetic resonance imaging (MRI) statistics and harnessing the skills of synthetic intelligence. The dataset consists of MRI pictures annotated with distinct phases of Alzheimer's, providing a robust basis for multi-class severity class. Advanced convolutional neural community (CNN) architectures, which include Xception, InceptionV3, ResNet50, and ResNet152, have been utilized to extract deep traits and ensure accurate categorization. These models are meticulously calibrated to discover the nuanced anatomical alterations in the brain related to numerous levels of the disorder. The EarlyStopping strategy is employed during training to forestall model overfitting and underfitting, as a result ensuring top of the line learning overall performance. The ensemble method, combining CNN architectures with the efficient NasNetMobile model, markedly improves predictive performance and achieves an great class accuracy of 0.992. This exquisite accuracy illustrates the ensemble's resilience in identifying subtle characteristics across different levels of Alzheimer's disease. The YoloV8 model is applied for object detection to identify Alzheimer's-specific biomarkers and structural defects in MRI snap shots, achieving a mean common precision (mAP) of 0.926. This high-precision detection enhances the model's reliability and practical usability. This AI-driven system automates the diagnostic procedure while offering a scalable, efficient, and interpretable solution for clinicians. Its super accuracy and detection abilities underscore its appropriateness for early analysis and monitoring, for this reason facilitating timely intervention measures. This approach integrates deep learning type and detection models, providing a complete solution for dermatological picture evaluation, hence enhancing the clinical diagnostic toolset for managing neurological diseases.