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Effective Features for Efficient Detection of Digestive Sys. Pathologies using Endoscopic Images

Sara Jabeen, Aqsa Jabeen, M Syed Adnan Shah

Abstract


Digestive System Pathology is gaining awareness now-a-days because of its growth rate. Currently working systems are not capable to detect the pathology at early stage. So, to overcome this problem we proposed a novel technique. The center capacity of the human stomach is a guide to processing. The four key segments of gastric stomach related capacity are its anything but a supply, corrosive emission, chemical discharge and its part in gastrointestinal motility. The supply limit of the stomach permits it to build its volume essentially while inward pressing factor increments just somewhat. Corrosive emission is a vital non-immunological safeguard against attacking microorganisms just as being a significant component for vertebrates to have more perplexing eating regimens. Discriminative features are obligatory for the accurately detection and classification of Gastrointestinal Track cancers. Other authors' observations and literature work is important to know for correctly detection of pathogenesis of the hemorrhagic infarct of the digestive system, mesenteric vein apoplexy, dangerous a ruptured appendix and cholecystitis. Discriminative features are obligatory for the accurately detection and classification of Gastrointestinal Track cancers. In this work, we perform the union of structural (Curvelet and Wavelet Transform) and textural (Local Binary Patterns LBP) feature descriptors. Purposed technique is executed on the CH-dataset. This dataset contains 56 normal and 120 abnormal endoscopic images. For classification we sent our model to the SVM classifier and SVM classifier came up with an accuracy of 89.2 %.

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DOI: https://doi.org/10.37628/ijods.v7i1.681

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