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Deep learning-based active contour technique with bagging and boosting algorithms hybrid approach for detecting bone Cancer from Mri scan images

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Abstract

Bone Cancer is a fatal disease caused by uncontrolled cell development. Following extensive research, about 100 different types of cancer have been discovered in the human body. Bone cancer is one of the most widely distributed of them, and it is fatal. The diagnosis of bone cancer is critical and has a low prognosis. For the process of medical picture analysis, some of the work is presently undertaken using data mining tools and image processing technology. Despite ongoing advancements in cancer research, it continues to be one of the world’s most lethal diseases. It is critical to pool all resources in order to achieve a breakthrough in cancer treatment. Early detection would go a long way toward lengthening the patient’s life and lowering the death rate. As a result, it is critical to develop approaches that are both inventive and efficient, as well as having fewer negative consequences. This research examines all aspects of bone cancer and its characteristics in order to determine its type. One of the most prominent methods for dividing up the Bone picture into distinct portions is the Active Contour Method. The study uses a Deep Learning-based Active Contour Method with bagging and boosting algorithms to achieve this goal. It is thought to be extremely difficult to extract critical information about the organ’s shapes and volumes from a photograph. However, advances in machine learning, particularly deep learning, have made it possible to segment images and classify sick regions more effectively. It entails a number of stages, including noise removal, segmentation, feature extraction, and selection. Pixels from the object’s edges are segmented. They are then processed to achieve the desired result. Finally, active cancer approaches were used to classify the MRI pictures, and the results were extremely accurate. To evaluate the success of the recommended strategy, we used three criteria: accuracy, specificity, and sensitivity. In terms of accuracy, specificity, and sensitivity, experiments show that the proposed system beats existing methods.

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All the data is collected from the simulation reports of the software and tools used by the authors. Authors are working on implementing the same using real world data with appropriate permissions.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, 600056, India

    Ediga Lingappa & L Rama Parvathy

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  1. Ediga Lingappa
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  2. L Rama Parvathy
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Correspondence to Ediga Lingappa.

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Lingappa, E., Parvathy, L.R. Deep learning-based active contour technique with bagging and boosting algorithms hybrid approach for detecting bone Cancer from Mri scan images. Multimed Tools Appl 82, 36363–36377 (2023). https://doi.org/10.1007/s11042-023-14811-5

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  • DOI: https://doi.org/10.1007/s11042-023-14811-5

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