Abstract
The most common primary intraocular childhood cancer that affects children’s and adults’ vision worldwide is retinoblastoma. It is uveal melanoma when contrasting and contrasting with adults. It is a lethal tumor that has the potential to enlarge and destroy the eye and the structures that surround it. As a result, early detection of retinoblastoma in young children is essential. The research's primary impact is the identification of retinal tumor cells. It's also important to determine the tumor's stages and group. Ophthalmologists can better predict and diagnose retinoblastoma cancer at an earlier stage based on the proposed systems. Utilizing machine learning techniques, this study proposes a novel technique for detecting retinoblastoma tumors utilizing radioactive polymeric material in Nanostructure analysis with ion beam based raman spectroscopy. The region of the tumor is examined using raman spectroscopy, and its characteristics are then extracted and categorized. Convolutional Principal kernel networks are used for the tumor feature extraction, and an ensemble of multilayer Q-regressive back propagation networks is used for the classification. For various retinoblastoma datasets, the experimental analysis is conducted in terms of training accuracy, RMSE, F_measure, recall, and AUC. The proposed technique attained training accuracy of 95%, RMSE of 55%, F_measure of 65%, recall of 58%, AUC of 49%.
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SS Conceived and design the analysis Writing- Original draft preparation. SRM Collecting the Data, DP Contributed data and analysis stools, AB Performed and analysis, MF Performed and analysis, SS Wrote the Paper, AA Editing and Figure Design.
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Sur, S., Sudhakara Reddy, M., Paikaray, D. et al. Nanostructure analysis in polymeric materials with ion beam based Raman spectroscopy for retinoblastoma tumor imaging using ensemble machine learning technique. Opt Quant Electron 55, 940 (2023). https://doi.org/10.1007/s11082-023-05167-z
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DOI: https://doi.org/10.1007/s11082-023-05167-z