{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T17:08:36Z","timestamp":1785431316659,"version":"3.56.0"},"reference-count":31,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T00:00:00Z","timestamp":1776211200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003093","name":"Malaysian Ministry of Higher Education","doi-asserted-by":"crossref","award":["FRGS\/1\/2023\/TK07\/UKM\/02\/7"],"award-info":[{"award-number":["FRGS\/1\/2023\/TK07\/UKM\/02\/7"]}],"id":[{"id":"10.13039\/501100003093","id-type":"DOI","asserted-by":"crossref"}]},{"award":["FRGS\/1\/2023\/TK07\/UKM\/02\/7"],"award-info":[{"award-number":["FRGS\/1\/2023\/TK07\/UKM\/02\/7"]}],"id":[{"id":"https:\/\/ror.org\/05mcs2t73","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Breast cancer continues to be one of the most serious and common afflictions affecting women around the globe. Despite ultrasound imaging being an effective method for the detection of abnormalities in dense breast tissues, there are a number of drawbacks when utilizing this method, including the subjective nature of the imaging and the variant nature of the imaging due to the cognitive biases of the interpreting expert and the experience of the interpreting expert. The above factors are the cause of the increased need in the implementation of AI-driven models for diagnostic analysis. In this research, we provide a hybrid deep learning-based framework for cancer classification of the breast cancer ultrasound image dataset (\u2018BUSI dataset\u2019). Methods: The contributing models of the proposed architecture involve the combination of a light ViT encoder and an EfficientNetV2-RW-S feature extractor. The combination mentioned leverage the positive sensitivities of the convolutional neural networks (CNNs) and the global reasoning neural networks (i.e., transformers) in the explanation of the architecture. The reason being, EfficientNetV2 diminishes the capture of the fine-grained morphological components of the lesions, edges, and echogenic variances of the tissue, whereas the transformer model diminishes the long-range dependencies of the lesions and other surrounding tissues. Results: The experimental results from the proposed hybrid model of the architecture demonstrates an enhanced classification accuracy of 97.95%, in contrast to the self-standing models of the architecture, the hybrid model supersedes the isolated ViT model (i.e., 89%) and the isolated CNN model (i.e., 80%) frameworks. Furthermore, the proposed model hybrid architecture also diminishes the overall self-attention computational complexity of the proposed model by substantially diminishing the number of tokens reaching an overall count of 10 (from the vast 197 tokens). This further leads to a substantial decrease in the memory and cost expended during the attention processes. Conclusions: Overall, this study proposes a method for the improved diagnostic and computational analysis, suggesting the proposed architecture to be a potential framework for use in the contemporary clinical environments.<\/jats:p>","DOI":"10.3390\/diagnostics16081176","type":"journal-article","created":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T14:02:33Z","timestamp":1776261753000},"page":"1176","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Hybrid Model for Ultrasound Image-Based Breast Cancer Diagnosis Using EfficientNet-V2 and Vision Transformer"],"prefix":"10.3390","volume":"16","author":[{"given":"Zainab","family":"Qahtan Mohammed","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Basic Education, Diyala University, Diyala 32001, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amel","family":"Tuama Alhussainy","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence Engineering Techniques, Technical Engineering College of Computer and AI\/Kirkuk, Northern Technical University, Kirkuk 36001, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0974-4271","authenticated-orcid":false,"given":"Ihsan","family":"Salman Jasim","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Basic Education, Diyala University, Diyala 32001, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Asraf","family":"Mohamed Moubark","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, University of Kebangsaan Malaysia, Bangi Selangor 43600, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,15]]},"reference":[{"key":"ref_1","first-page":"524","article-title":"Breast cancer statistics, 2022","volume":"72","author":"Giaquinto","year":"2022","journal-title":"CA Cancer J. 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