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              <text>An Innovative Approach for Osteosarcoma Bone Cancer Detection based on Attention Embedded R-CNN Approach</text>
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              <text>Medical Image; Osteosarcoma; Residual Progressive Feature Extraction (RPFE)</text>
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              <text>The malignant bone tumor osteosarcoma. Any bone is at risk, but lengthy bones like the limbs are more vulnerable. Although the precise cause of this malignant growth is uncertain, experts concur that it is caused by changes to deoxyribonucleic acid (DNA) inside the bones. This can cause the breakdown of good tissue and the growth of aberrant, pathological bone. Osteosarcoma has a 76% cure rate if detected early and treated before it spreads to other parts of the body. An X-ray is the primary tool for detecting bone tumors. Bone X-rays and other imaging tests can help detect osteosarcoma. A biopsy should be performed for an accurate diagnosis. This is a time-consuming and tedious task that might be greatly reduced with the help of appropriate tools. Data preprocessing, segmentation, feature extraction, and model training are the four main pillars of the proposed approach. Unwanted noises can be filtered out with some preprocessing. Low-spatial-frequency and high-spatial-frequency components are separated using segmentation. The proposed approach employed Tumor Border Clarity, Joint Distance, Tumor Texture, and other features for feature extraction. Let's move on to A-Residual CNN model training. The success percentage of the proposed approach was 96.39 percent.  2023 IEEE.</text>
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              <text>Prasad B.M.G.; Bikash Acharjee P.; Guntakala S.; Sharma D.; Divya N.; Patil H.</text>
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              <text>International Conference on Sustainable Communication Networks and Application, ICSCNA 2023 - Proceedings, pp. 1431-1436.</text>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <text>2023-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1109/ICSCNA58489.2023.10370651" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICSCNA58489.2023.10370651&lt;/a&gt;
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              <text>Restricted Access</text>
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              <text>ISBN: 979-835031398-7</text>
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              <text>Online</text>
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              <text>Prasad B.M.G., Ravindra College of Engineering for Women (RCEW, Autonomous), Department of Cse, Andhra Pradesh, Kurnool, 518452, India; Bikash Acharjee P., Christ University, Pune, India; Guntakala S., K.B.N College, Department of Mca, Andhra Pradesh, Vijayawada, India; Sharma D., Institute of Pharmacy, Assam Don Bosco University, Tapesi, Assam, India; Divya N., Prince Shri Venkateshwara Padmavathy Engineering College, Department of Civil Engineering, Tamilnadu, Chennai, India; Patil H., Symbiosis Institute of Technology, Symbiosis International (Deemed University), Maharashtra, Pune, India</text>
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