Recent advances in the field of digital endoscopy have recognized as a primary key knowledge for medical screening of presence of diseases at its earlier stages and minimal invasive surgery. Many research works are done on automatic analyzing and detection of early esophageal adenocarcinoma which is not easy to detect at its primary stage. Because the esophageal images are naturally indeterministic and it is uncertain to detect its earliest appearance more precisely at its right stage of

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... is. Thus, in this present research work the statistical information-based grid clustering is developed by empowering its clustering capability using Quantum mechanism. The conventional STatistical INformation Grid Clustering (STING) reduces the computation complexity of the clustering process, but outlier detection and uncertainty handling are very challenging, because it partitions the Eshophageal image in the layer by layer levels. Each layer is divided into cells and they are known as child cells. The relevant child cells alone are used to determine the statistical information about the entire image and based on the spatial information the similarity measure among two images are determined. The distance measure and the neighborhood pixel information are computing using quantum theory to overcome the uncertainty and parallel processing is done for concurrency based early esophageal adenocarcinoma prediction. The Barret's Esophageal images are used for clustering the cancerous and noncancerous images. The simulation results proved that the proposed Quantum STatistical INformation Grid Clustering produce better result in detection of Early Esophageal Adenocarcinoma detection compared to other standard clustering models.