Authors : Bilal Shabbir Qaisar; Mahammad Ali Shahid; Sunil Ashraf; Muhammad Adnan; M. Mudasar Azeem; Maham Ali; Muhammad Nauman

Volume/Issue : Volume 10 - 2025, Issue 8 - August

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Google Scholar : https://tinyurl.com/ycypvcy3

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DOI : https://doi.org/10.38124/ijisrt/25aug1009

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Abstract : Imaging techniques are widely used for medical diagnostics. This can sometimes lead to a real bottleneck when there is a shortage of medical practitioners, and the images must be manually processed. In such a situation, there is a need to reduce the amount of manual work by automating part of the analysis. In this study, we investigate the potential of a machine-learning algorithm for trauma detection in medical image processing. A new method called ResNet50V2 was developed on the trauma dataset to detect trauma disease. We compare the results of the new method analysis with other state-of-the-art networks. The proposed base model, ResNet50V2, received a score of 99.40%.

Keywords : Machine Learning; ResNet50V2; Trauma; Medical Images.

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