Khaled Obaideen
ABSTRACT
This study delves into the burgeoning field of Siamese Networks (SNs) within biomedical (BM) research, presenting a comprehensive analysis of 319 scholarly articles published from 1973 to 2023. It chronicles the field's journey, starting from a period of minimal activity to a remarkable surge in research and publications beginning in 2012, peaking in 2022. This progressive era is quantified by a noteworthy mean citation index of 8.741 per article, highlighting the significant academic impact of these works. Employing the Scopus database, alongside analytical tools such as Bibliometrix, Microsoft Excel 2016, and VOSviewer, the study meticulously examines the extensive literature to extract key narratives and data patterns. A striking finding is the prevalent culture of collaboration within the field, evidenced by 18.35% of the articles featuring international co-authorships, thereby reinforcing the global and interdisciplinary nature of SNs. The research underscores the crucial role of deep learning (DL) in medicine, particularly emphasizing the innovative applications of SNs in medical imaging (MI) and their adeptness in decoding genetic information and interpreting complex BM signals. The potential of these networks to transform diagnostic processes is critically evaluated. Overall, this paper advocates for the significant contribution of SNs in the realm of BM research, accentuating their role in bridging advanced computational techniques with life-saving medical innovations, thereby marking a new era in diagnostic and treatment methodologies.
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