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Medtransnet: advanced gating transformer network for medical image classification

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Abstract

Accurate medical image classification poses a significant challenge in designing expert computer-aided diagnosis systems. While deep learning approaches have shown remarkable advancements over traditional techniques, addressing inter-class similarity and intra-class dissimilarity across medical imaging modalities remains challenging. This work introduces the advanced gating transformer network (MedTransNet), a deep learning model tailored for precise medical image classification. MedTransNet utilizes channel and multi-gate attention mechanisms, coupled with residual interconnections, to learn category-specific attention representations from diverse medical imaging modalities. Additionally, the use of gradient centralization during training helps in preventing overfitting and improving generalization, which is especially important in medical imaging applications where the availability of labeled data is often limited. Evaluation on benchmark datasets, including APTOS-2019, Figshare, and SARS-CoV-2, demonstrates effectiveness of the proposed MedTransNet across tasks such as diabetic retinopathy severity grading, multi-class brain tumor classification, and COVID-19 detection. Experimental results showcase MedTransNet achieving 85.68% accuracy for retinopathy grading, 98.37% (\(\pm \,0.44\)) for tumor classification, and 99.60% for COVID-19 detection, surpassing recent deep learning models. MedTransNet holds promise for significantly improving medical image classification accuracy.

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Authors and Affiliations

  1. Georgia State University, Atlanta, GA, USA

    Nagur Shareef Shaik & Teja Krishna Cherukuri

  2. Vignan’s Foundation for Science Technology and Research, Vadlamudi, AndhraPradesh, India

    N Veeranjaneulu & Jyostna Devi Bodapati

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  1. Nagur Shareef Shaik
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  2. Teja Krishna Cherukuri
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  3. N Veeranjaneulu
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  4. Jyostna Devi Bodapati
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Contributions

The authors of this work, NSS, TKC, VN, and JDB, made the following contributions to the project: NSS: Played a key role in conceptualizing the project and designing the methodology. He conducted data collection and analysis, and contributed to writing the original draft of the manuscript. TKC: Conducted an extensive literature review and performed background research. He was responsible for interpreting and visualizing the data, as well as reviewing and editing the manuscript. VN: contributed to the experimental design and implementation. He conducted statistical analysis of the data and actively participated in revising the manuscript. JDB: Played a significant role in software development and coding. She validated and tested the experimental results and provided valuable input during the preparation of the manuscript. All authors contributed equally to the research project, reviewing and approving the final version of the manuscript.

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Correspondence to Jyostna Devi Bodapati.

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Shaik, N.S., Cherukuri, T.K., Veeranjaneulu, N. et al. Medtransnet: advanced gating transformer network for medical image classification. Machine Vision and Applications 35, 73 (2024). https://doi.org/10.1007/s00138-024-01542-2

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