Deep Learning and Transfer Learning Techniques For Alzheimer's Detection From Mri Image

Abstract

Alzheimer's disease is a prevalent neurological condition affecting millions globally, necessitating early identification for effective management and timely intervention. Recently, deep learning techniques have emerged as valuable tools for evaluating medical images and aiding in disease detection. This study introduces an innovative approach for Alzheimer's disease detection based on MRI images, integrating deep learning and transfer learning techniques. Aim to explores and compares the performance of popular deep learning architectures, including ResNet50, VGG19, Xception, and DenseNet169, in the context of image classification tasks. Leveraging the advancements in artificial intelligence, these architectures are evaluated on a diverse dataset to assess their accuracy and efficiency. Surprisingly, the investigation reveals that a carefully crafted custom convolutional neural network (CNN) architecture surpasses the established models, achieving an outstanding dataset accuracy of 97%. The study begins with an in-depth review of the aforementioned state-of-the-art architectures, highlighting their strengths and weaknesses. Each model is implemented and fine-tuned on a benchmark dataset to ensure a fair comparison. Results indicate that while ResNet50, VGG19, Xception, and DenseNet169 exhibit commendable performance, the custom CNN architecture consistently outperforms them, showcasing the potential benefits of tailored model design. The custom CNN architecture is meticulously detailed, providing insights into its unique architectural choices and hyperparameter configurations. The thesis delves into the rationale behind each design decision, shedding light on how the model optimally captures complex patterns within the dataset. This detailed analysis not only contributes to the understanding of deep learning principles but also presents a valuable framework for future custom architecture development. Furthermore, the study explores the transferability of knowledge gained from the custom CNN to other domains, emphasizing the adaptability and generalization

capabilities of the proposed architecture. Practical implications and potential applications in real- world scenarios are discussed, demonstrating the significance of the research findings beyond the

scope of the current dataset. In conclusion, this thesis presents a comprehensive evaluation of prominent deep learning architectures and introduces a novel custom CNN architecture that outperforms established models in image classification tasks. The research contributes to the ongoing discourse in artificial intelligence by showcasing the potential of tailored model design and offering valuable insights for future advancements in the field. Keyword: CNN, Recall, Alzheimer's disease, MRI pictures, Transfer Learning.