The major mortality factor relevant to the intestinal tract is the growth of tumorous cells(polyps)in various parts.More specifically,colonic polyps have a high rate and are recognized as a precursor of colon cancer g...The major mortality factor relevant to the intestinal tract is the growth of tumorous cells(polyps)in various parts.More specifically,colonic polyps have a high rate and are recognized as a precursor of colon cancer growth.Endoscopy is the conventional technique for detecting colon polyps,and considerable research has proved that automated diagnosis of image regions that might have polyps within the colon might be used to help experts for decreasing the polyp miss rate.The automated diagnosis of polyps in a computer-aided diagnosis(CAD)method is implemented using statistical analysis.Nowadays,Deep Learning,particularly throughConvolution Neural networks(CNN),is broadly employed to allowthe extraction of representative features.This manuscript devises a new Northern Goshawk Optimization with Transfer Learning Model for Colonic Polyp Detection and Classification(NGOTL-CPDC)model.The NGOTL-CPDC technique aims to investigate endoscopic images for automated colonic polyp detection.To accomplish this,the NGOTL-CPDC technique comprises of adaptive bilateral filtering(ABF)technique as a noise removal process and image pre-processing step.Besides,the NGOTL-CPDC model applies the Faster SqueezeNet model for feature extraction purposes in which the hyperparameter tuning process is performed using the NGO optimizer.Finally,the fuzzy Hopfield neural network(FHNN)method can be employed for colonic poly detection and classification.A widespread simulation analysis is carried out to ensure the improved outcomes of the NGOTL-CPDC model.The comparison study demonstrates the enhancements of the NGOTL-CPDC model on the colonic polyp classification process on medical test images.展开更多
This case reports a 63-year-old male patient who was admitted to the hospital due to the discovery of sigmoid colon protrusion during physical examination.EUS indicated submucosal lesions,and lymphangioma was initiall...This case reports a 63-year-old male patient who was admitted to the hospital due to the discovery of sigmoid colon protrusion during physical examination.EUS indicated submucosal lesions,and lymphangioma was initially considered.Colonoscopy revealed a 0-Is+Ⅱc type protrusion,with a smooth surface and visible dendritic vessels.The biopsy was tough.Auxiliary imaging showed no distant metastasis or obvious organ damage,and the laboratory test results were normal.The pathological confirmation after submucosal dissection was non-Hodgkin's small B-cell lymphoma(MALT lymphoma).Combining imaging,pathological and immunohistochemical analyses,this case emphasizes the precise sampling and classification strategy for submucosal masses in the digestive tract,suggesting that endoscopic biopsy is difficult to fully reflect submucosal infiltration and should be supplemented by EUS and large tissue sampling.In terms of treatment,based on the local lesion,pathological classification and the patient's overall condition,a strategy combining endoscopic dissection and subsequent chemotherapy is selected to achieve a balance between local control and systemic intervention.展开更多
文摘The major mortality factor relevant to the intestinal tract is the growth of tumorous cells(polyps)in various parts.More specifically,colonic polyps have a high rate and are recognized as a precursor of colon cancer growth.Endoscopy is the conventional technique for detecting colon polyps,and considerable research has proved that automated diagnosis of image regions that might have polyps within the colon might be used to help experts for decreasing the polyp miss rate.The automated diagnosis of polyps in a computer-aided diagnosis(CAD)method is implemented using statistical analysis.Nowadays,Deep Learning,particularly throughConvolution Neural networks(CNN),is broadly employed to allowthe extraction of representative features.This manuscript devises a new Northern Goshawk Optimization with Transfer Learning Model for Colonic Polyp Detection and Classification(NGOTL-CPDC)model.The NGOTL-CPDC technique aims to investigate endoscopic images for automated colonic polyp detection.To accomplish this,the NGOTL-CPDC technique comprises of adaptive bilateral filtering(ABF)technique as a noise removal process and image pre-processing step.Besides,the NGOTL-CPDC model applies the Faster SqueezeNet model for feature extraction purposes in which the hyperparameter tuning process is performed using the NGO optimizer.Finally,the fuzzy Hopfield neural network(FHNN)method can be employed for colonic poly detection and classification.A widespread simulation analysis is carried out to ensure the improved outcomes of the NGOTL-CPDC model.The comparison study demonstrates the enhancements of the NGOTL-CPDC model on the colonic polyp classification process on medical test images.
文摘This case reports a 63-year-old male patient who was admitted to the hospital due to the discovery of sigmoid colon protrusion during physical examination.EUS indicated submucosal lesions,and lymphangioma was initially considered.Colonoscopy revealed a 0-Is+Ⅱc type protrusion,with a smooth surface and visible dendritic vessels.The biopsy was tough.Auxiliary imaging showed no distant metastasis or obvious organ damage,and the laboratory test results were normal.The pathological confirmation after submucosal dissection was non-Hodgkin's small B-cell lymphoma(MALT lymphoma).Combining imaging,pathological and immunohistochemical analyses,this case emphasizes the precise sampling and classification strategy for submucosal masses in the digestive tract,suggesting that endoscopic biopsy is difficult to fully reflect submucosal infiltration and should be supplemented by EUS and large tissue sampling.In terms of treatment,based on the local lesion,pathological classification and the patient's overall condition,a strategy combining endoscopic dissection and subsequent chemotherapy is selected to achieve a balance between local control and systemic intervention.
