Innovation in learning algorithms has made retinal vessel segmentation and automatic grading tech-niques crucial for clinical diagnosis and prevention of diabetic retinopathy.The traditional methods struggle with accu...Innovation in learning algorithms has made retinal vessel segmentation and automatic grading tech-niques crucial for clinical diagnosis and prevention of diabetic retinopathy.The traditional methods struggle with accuracy and reliability due to multi-scale variations in retinal blood vessels and the complex pathological relationship in fundus images associated with diabetic retinopathy.While the single-modal diabetic retinopathy grading network addresses class imbalance challenges and lesion representation in fundus image data,dual-modal diabetic retinopathy grading methods offer superior performance.However,the scarcity of dual-modal data and the lack of effective feature fusion methods limit their potential due to multi-scale variations.This paper addresses these issues by focusing on multi-scale retinal vessel segmentation,dual feature fusion,data augmentation,and attention-based grading.The proposed model aims to improve comprehensive segmentation for retinal images with varying vessel thicknesses.It employs a dual-branch parallel architecture that integrates a transformer encoder with a convolutional neural network encoder to extract local and global information for synergistic saliency learning.Besides that,the model uses residual structures and attention modules to extract critical lesions,enhancing the accuracy and reliability of diabetic retinopathy grading.To evaluate the efficacy of the proposed approach,this study compared it with other pre-trained publicly open models,ResNet152V2,ConvNext,Efficient Net,DenseNet,and Swin Transform,with the same developmental parameters.All models achieved approximately 85%accuracy with the same image preparation method.However,the proposed approach outperforms and optimizes existing models by achieving an accuracy of 99.17%,99.04%,and 99.24%,on Kaggle APTOS19,IDRiD,and EyePACS datasets,respectively.These results support the model’s utility in helping ophthalmologists diagnose diabetic retinopathy more rapidly and accurately.展开更多
Helicobacter pylori(H.pylori)infection has a protective effect on gastroesophageal reflux disease(GERD).Both of these diseases have a very high incidence and prevalence.As a result,GERD often recurs after anti-Helicob...Helicobacter pylori(H.pylori)infection has a protective effect on gastroesophageal reflux disease(GERD).Both of these diseases have a very high incidence and prevalence.As a result,GERD often recurs after anti-Helicobacter therapy.The problem of effective treatment of H.pylori infection and GERD is that the main groups of drugs[proton pump inhibitors(PPIs)and potassium-competitive acid blockers]have the possibility of side effects with use.Such supposed side effects have no evidence in randomized controlled trials that comply with the principles of evidence-based medicine.Morphological changes in the gastric mucosa after long-term use of antisecretory drugs should be considered as compensatory mechanisms of sanogenesis.The greatest concern for doctors who treat patients with antisecretory drugs is the risk of gastric carcinogenesis.This article presents an analysis of morphological and pathophysiological changes that occur after long-term use of antisecretory drugs(PPIs).Hypertrophy(hyperplasia)of G cells,enterochromaffin-like cells and possible fundic gland polyps(hyperplasia)are compensatory mechanisms of sanogenesis during long-term treatment with PPIs.These mechanisms are of primary importance for rehabilitation and prevention of complications in patients with GERD,non-steroidal anti-inflammatory drugsgastropathy and other diseases during long-term treatment with PPIs.Understanding the pathophysiological and morphological mechanisms of compensation and adaptation,the mechanisms of sanogenesis and carcinogenesis will increase the number of indications for long-term use of PPIs with a high level of efficiency and safety of treatment.In addition,understanding the pathophysiological and morphological mechanisms of compensation and adaptation,the mechanisms of sanogenesis will allow us to forecast the side effects of long-term use of potassium-competitive acid blockers.展开更多
基金funded by Princess Nourah bint Abdulrahman University and Researchers Supporting Project number(PNURSP2025R346)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Innovation in learning algorithms has made retinal vessel segmentation and automatic grading tech-niques crucial for clinical diagnosis and prevention of diabetic retinopathy.The traditional methods struggle with accuracy and reliability due to multi-scale variations in retinal blood vessels and the complex pathological relationship in fundus images associated with diabetic retinopathy.While the single-modal diabetic retinopathy grading network addresses class imbalance challenges and lesion representation in fundus image data,dual-modal diabetic retinopathy grading methods offer superior performance.However,the scarcity of dual-modal data and the lack of effective feature fusion methods limit their potential due to multi-scale variations.This paper addresses these issues by focusing on multi-scale retinal vessel segmentation,dual feature fusion,data augmentation,and attention-based grading.The proposed model aims to improve comprehensive segmentation for retinal images with varying vessel thicknesses.It employs a dual-branch parallel architecture that integrates a transformer encoder with a convolutional neural network encoder to extract local and global information for synergistic saliency learning.Besides that,the model uses residual structures and attention modules to extract critical lesions,enhancing the accuracy and reliability of diabetic retinopathy grading.To evaluate the efficacy of the proposed approach,this study compared it with other pre-trained publicly open models,ResNet152V2,ConvNext,Efficient Net,DenseNet,and Swin Transform,with the same developmental parameters.All models achieved approximately 85%accuracy with the same image preparation method.However,the proposed approach outperforms and optimizes existing models by achieving an accuracy of 99.17%,99.04%,and 99.24%,on Kaggle APTOS19,IDRiD,and EyePACS datasets,respectively.These results support the model’s utility in helping ophthalmologists diagnose diabetic retinopathy more rapidly and accurately.
文摘Helicobacter pylori(H.pylori)infection has a protective effect on gastroesophageal reflux disease(GERD).Both of these diseases have a very high incidence and prevalence.As a result,GERD often recurs after anti-Helicobacter therapy.The problem of effective treatment of H.pylori infection and GERD is that the main groups of drugs[proton pump inhibitors(PPIs)and potassium-competitive acid blockers]have the possibility of side effects with use.Such supposed side effects have no evidence in randomized controlled trials that comply with the principles of evidence-based medicine.Morphological changes in the gastric mucosa after long-term use of antisecretory drugs should be considered as compensatory mechanisms of sanogenesis.The greatest concern for doctors who treat patients with antisecretory drugs is the risk of gastric carcinogenesis.This article presents an analysis of morphological and pathophysiological changes that occur after long-term use of antisecretory drugs(PPIs).Hypertrophy(hyperplasia)of G cells,enterochromaffin-like cells and possible fundic gland polyps(hyperplasia)are compensatory mechanisms of sanogenesis during long-term treatment with PPIs.These mechanisms are of primary importance for rehabilitation and prevention of complications in patients with GERD,non-steroidal anti-inflammatory drugsgastropathy and other diseases during long-term treatment with PPIs.Understanding the pathophysiological and morphological mechanisms of compensation and adaptation,the mechanisms of sanogenesis and carcinogenesis will increase the number of indications for long-term use of PPIs with a high level of efficiency and safety of treatment.In addition,understanding the pathophysiological and morphological mechanisms of compensation and adaptation,the mechanisms of sanogenesis will allow us to forecast the side effects of long-term use of potassium-competitive acid blockers.
