eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2026-04-21 19 2 71438 article Upendra Singh 1 Vidit Kumar 1 Bhaskar Pant 1 Department of Computer Science and Engineering, Graphic Era (Deemed to be University), Dehradun, India. Alzheimer's disease (AD) presents a neurological challenge, requiring accurate and early diagnosis for effective intervention. In this study an efficient deep learning (DL) framework is proposed, which integrates spatial attention mechanisms with the ResNet50 core network to improve the classification of cognitive conditions, including AD, mild cognitive impairment (MCI), and normal cognitive impairment (CN). The novel aspect of our work lies in the strategic integration of two dedicated spatial attention blocks within the middle and deep layers of the pre-trained ResNet50 backbone. This architecture improves traditional feature extraction by capturing spatial dependencies and focusing the model on relevant regions. We also introduced two dedicated convolutional feature extraction blocks that receive input from the intermediate layers of the ResNet50 and allowing the model to leverage both high-level contextual information and fine-grained positional spatial properties simultaneously. Out efficient and attention-enhancing design significantly improves discriminatory power. The proposed model demonstrates excellent performance, achieving an accuracy rate of 88.46% (AD vs. MCI vs. CN) and 89.23% (AD vs. CN) with the ADNI dataset, which is a significant improvement over previous methods. These results demonstrate the benefit of incorporating spatial attention into deep convolutional neural networks for AD diagnosis. https://biomedpharmajournal.org/vol19no2/att-convfe-resnetaspatialattentionandmulti-scale-feature-extraction-framework-for-alzheimers-disease-diagnosis/ Artificial intelligence(AI) Convolutional Feature Extractor Blocks(CFEB) Spatial Attention Block(SAB) Deep Learning(DL) Alzheimer’s Disease Neuroimaging Initiative(ADNI) Positron Emission Tomography(PET) Machine learning(ML) Image-Classification