eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2025-11-05 18 4 68868 article Safdar Sardar Khan 1 Rijvan Beg 1 Shredha Parmar 1 Mohammed Irfan Khan 1 Arpit Deo 1 Sadaf Akhtar 1 Department of computer science and engineering, Medicaps University, Indore MP, India Department of computer science and engineering, SRM University, Amravati, Andhra Pradesh, India This study aims to evaluate the clinical efficacy of machine learning and deep learning models, particularly EfficientNetB7, for the early diagnosis of Alzheimer's disease using neuroimaging and clinical data. Machine learning and deep learning approaches to evaluate complex data, including neuroimaging and clinical information, aiding in the timely detection of Alzheimer's disease and delivering precise diagnoses. Machine learning algorithms, through comprehensive analysis of diverse data sources, have the capacity to discern intricate patterns and features that may elude human observation. Through training on extensive datasets, these models achieve heightened accuracy and improved generalization in diagnosing Alzheimer's disease. Deep learning, a subset of machine learning characterized by neural networks with numerous layers, enhances the capacity to decipher and extract nuanced patterns from extensive and intricate datasets. The development of reliable machine learning and deep learning models holds significant potential for enhancing early detection, a pivotal factor for timely interventions and appropriate treatment plans. Early diagnosis can lead to more effective disease management, maybe improving the standard of life for those suffering from Alzheimer's disease and reducing the strain on the healthcare system and carers. In a performance evaluation, the EfficientNetB7 model demonstrated exceptional accuracy, achieving a score of 99.36%. This underscores its superior performance in comparison to other models. Furthermore, it displayed a validation accuracy of 49.48%, showcasing its robustness and capability. The model maintained a validation loss of 2.9, while the training loss was impressively low at 0.017. When benchmarked against other machine learning models, EfficientNetB7 consistently outperforms, underscoring its potential for elevating diagnostic accuracy in Alzheimer's disease detection. https://biomedpharmajournal.org/vol18octoberspledition/ai-assisted-early-diagnosis-of-alzheimers-disease-a-clinical-evaluation-of-machine-and-deep-learning-models/ Alzheimer's disease detection Deep learning EfficientNetB7 Health care Machine learning