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Advancements in Image Enhancement and Attention based EfficientDet Optimization Classifier for Precise Osteosarcoma Lung Nodule Detection

By
Nandhini. A. ,
Nandhini. A.

Assistant Professor SG, Department of Computer Applications, Nehru College of Management, Coimbatore

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Sengaliappan M. ,
Sengaliappan M.

Associate Professor & Head, Department of Computer Applications, Nehru College of Management, Coimbatore

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Abstract

Introduction: Osteosarcoma is a malignant bone tumor that frequently spreads to the lungs, hence therapy effectiveness depends on early identification. However, noise and subtle characteristics still pose a challenge for reliable Lung Nodules Detection (LND) in medical pictures. In earlier work, SSD-VGG16 was implemented to provide a bounding box with an accuracy score that represented a single osteosarcoma nodule. Increasing model complexity is sometimes necessary to achieve improved accuracy with current approaches, which might worsen their computing inefficiencies.
Methods: For accurate osteosarcoma lung nodule identification, this study offers the hybrid Dynamic Virtual Bats Algorithm with Attention based Efficient Object identification (A- EfficientDet). In order to improve the quality and informativeness of clinical pictures, this study suggests including Chebyshev filtering into the pre-processing pipeline. It focuses on CT scans for the purpose of detecting lung nodules associated with osteosarcoma. Additionally, provide the optimized A-EfficientDet model, a hybrid EfficientDet model improved using the DVBA optimization technique for accurate lung nodule identification.
Results: The effectiveness of the suggested strategy in attaining accurate osteosarcoma LND is demonstrated by the experimental findings. Chebyshev filtering is incorporated during the pre-processing step, which leads to more accurate detection findings by improving the signal-to-noise ratio (SNR) and lung nodule visibility.
Conclusion: Additionally, the improved EfficientDet model demonstrates its suitability for clinical applications in early osteosarcoma detection and treatment monitoring by achieving (SOTA) State-Of-The-Art execution by the metrics of sensitivity, specificity, and F1 score.

How to Cite

1.
Nandhini. A, Sengaliappan M. Advancements in Image Enhancement and Attention based EfficientDet Optimization Classifier for Precise Osteosarcoma Lung Nodule Detection. Salud, Ciencia y Tecnología - Serie de Conferencias [Internet]. 2024 Jun. 21 [cited 2024 Jul. 19];3:936. Available from: https://conferencias.saludcyt.ar/index.php/sctconf/article/view/936

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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