Background:Early and accurate diagnosis of cataracts,which ranks among the leading preventable causes of blindness,is critical to securing positive outcomes for patients.Recently,eye image analyses have used deep lear...Background:Early and accurate diagnosis of cataracts,which ranks among the leading preventable causes of blindness,is critical to securing positive outcomes for patients.Recently,eye image analyses have used deep learning(DL)approaches to automate cataract classification more precisely,leading to the development of the Multiscale Parallel Feature Aggregation Network with Attention Fusion(MPFAN-AF).Focused on improving a model’s performance,this approach applies multiscale feature extraction,parallel feature fusion,along with attention-based fusion to sharpen its focus on salient features,which are crucial in detecting cataracts.Methods:Coarse-level features are captured through the application of convolutional layers,and these features undergo refinement through layered kernels of varying sizes.Moreover,this method captures all the diverse representations of cataracts accurately by parallel feature aggregation.Utilizing the Cataract Eye Dataset available on Kaggle,containing 612 labelled images of eyes with and without cataracts proportionately(normal vs.pathological),this model was trained and tested.Results:Results using the proposed model reflect greater precision over traditional convolutional neural networks(CNNs)models,achieving a classification accuracy of 97.52%.Additionally,the model demonstrated exceptional performance in classification tasks.The ablation studies validated that all applications added value to the prediction process,particularly emphasizing the attention fusion module.Conclusion:The MPFAN-AF model demonstrates high efficiency together with interpretability because it shows promise as an integration solution for real-time mobile cataract detection screening systems.Standard performance indicators indicate that AI-based ophthalmology tools have a promising future for use in remote conditions that lack medical resources.展开更多
The use of Explainable Artificial Intelligence(XAI)models becomes increasingly important for making decisions in smart healthcare environments.It is to make sure that decisions are based on trustworthy algorithms and ...The use of Explainable Artificial Intelligence(XAI)models becomes increasingly important for making decisions in smart healthcare environments.It is to make sure that decisions are based on trustworthy algorithms and that healthcare workers understand the decisions made by these algorithms.These models can potentially enhance interpretability and explainability in decision-making processes that rely on artificial intelligence.Nevertheless,the intricate nature of the healthcare field necessitates the utilization of sophisticated models to classify cancer images.This research presents an advanced investigation of XAI models to classify cancer images.It describes the different levels of explainability and interpretability associated with XAI models and the challenges faced in deploying them in healthcare applications.In addition,this study proposes a novel framework for cancer image classification that incorporates XAI models with deep learning and advanced medical imaging techniques.The proposed model integrates several techniques,including end-to-end explainable evaluation,rule-based explanation,and useradaptive explanation.The proposed XAI reaches 97.72%accuracy,90.72%precision,93.72%recall,96.72%F1-score,9.55%FDR,9.66%FOR,and 91.18%DOR.It will discuss the potential applications of the proposed XAI models in the smart healthcare environment.It will help ensure trust and accountability in AI-based decisions,which is essential for achieving a safe and reliable smart healthcare environment.展开更多
Pupil dynamics are the important characteristics of face spoofing detection.The face recognition system is one of the most used biometrics for authenticating individual identity.The main threats to the facial recognit...Pupil dynamics are the important characteristics of face spoofing detection.The face recognition system is one of the most used biometrics for authenticating individual identity.The main threats to the facial recognition system are different types of presentation attacks like print attacks,3D mask attacks,replay attacks,etc.The proposed model uses pupil characteristics for liveness detection during the authentication process.The pupillary light reflex is an involuntary reaction controlling the pupil’s diameter at different light intensities.The proposed framework consists of two-phase methodologies.In the first phase,the pupil’s diameter is calculated by applying stimulus(light)in one eye of the subject and calculating the constriction of the pupil size on both eyes in different video frames.The above measurement is converted into feature space using Kohn and Clynes model-defined parameters.The Support Vector Machine is used to classify legitimate subjects when the diameter change is normal(or when the eye is alive)or illegitimate subjects when there is no change or abnormal oscillations of pupil behavior due to the presence of printed photograph,video,or 3D mask of the subject in front of the camera.In the second phase,we perform the facial recognition process.Scale-invariant feature transform(SIFT)is used to find the features from the facial images,with each feature having a size of a 128-dimensional vector.These features are scale,rotation,and orientation invariant and are used for recognizing facial images.The brute force matching algorithm is used for matching features of two different images.The threshold value we considered is 0.08 for good matches.To analyze the performance of the framework,we tested our model in two Face antispoofing datasets named Replay attack datasets and CASIA-SURF datasets,which were used because they contain the videos of the subjects in each sample having three modalities(RGB,IR,Depth).The CASIA-SURF datasets showed an 89.9%Equal Error Rate,while the Replay Attack datasets showed a 92.1%Equal Error Rate.展开更多
Purpose:The purpose of this work is to present an approach for autonomous detection of eye disease in fundus images.Furthermore,this work presents an improved variant of the Tiny YOLOv7 model developed specifically fo...Purpose:The purpose of this work is to present an approach for autonomous detection of eye disease in fundus images.Furthermore,this work presents an improved variant of the Tiny YOLOv7 model developed specifically for eye disease detection.The model proposed in this work is a highly useful tool for the development of applications for autonomous detection of eye diseases in fundus images that can help and assist ophthalmologists.Design/methodology/approach:The approach adopted to carry out this work is twofold.Firstly,a richly annotated dataset consisting of eye disease classes,namely,cataract,glaucoma,retinal disease and normal eye,was created.Secondly,an improved variant of the Tiny YOLOv7 model was developed and proposed as EYE-YOLO.The proposed EYE-YOLO model has been developed by integrating multi-spatial pyramid pooling in the feature extraction network and Focal-EIOU loss in the detection network of the Tiny YOLOv7 model.Moreover,at run time,the mosaic augmentation strategy has been utilized with the proposed model to achieve benchmark results.Further,evaluations have been carried out for performance metrics,namely,precision,recall,F1 Score,average precision(AP)and mean average precision(mAP).Findings:The proposed EYE-YOLO achieved 28%higher precision,18%higher recall,24%higher F1 Score and 30.81%higher mAP than the Tiny YOLOv7 model.Moreover,in terms of AP for each class of the employed dataset,it achieved 9.74%higher AP for cataract,27.73%higher AP for glaucoma,72.50%higher AP for retina disease and 13.26%higher AP for normal eye.In comparison to the state-of-the-art Tiny YOLOv5,Tiny YOLOv6 and Tiny YOLOv8 models,the proposed EYE-YOLO achieved 6:23.32%higher mAP.Originality/value:This work addresses the problem of eye disease recognition as a bounding box regression and detection problem.Whereas,the work in the related research is largely based on eye disease classification.The other highlight of this work is to propose a richly annotated dataset for different eye diseases useful for training deep learning-based object detectors.The major highlight of this work lies in the proposal of an improved variant of the Tiny YOLOv7 model focusing on eye disease detection.The proposed modifications in the Tiny YOLOv7 aided the proposed model in achieving better results as compared to the state-of-the-art Tiny YOLOv8 and YOLOv8 Nano.展开更多
文摘Background:Early and accurate diagnosis of cataracts,which ranks among the leading preventable causes of blindness,is critical to securing positive outcomes for patients.Recently,eye image analyses have used deep learning(DL)approaches to automate cataract classification more precisely,leading to the development of the Multiscale Parallel Feature Aggregation Network with Attention Fusion(MPFAN-AF).Focused on improving a model’s performance,this approach applies multiscale feature extraction,parallel feature fusion,along with attention-based fusion to sharpen its focus on salient features,which are crucial in detecting cataracts.Methods:Coarse-level features are captured through the application of convolutional layers,and these features undergo refinement through layered kernels of varying sizes.Moreover,this method captures all the diverse representations of cataracts accurately by parallel feature aggregation.Utilizing the Cataract Eye Dataset available on Kaggle,containing 612 labelled images of eyes with and without cataracts proportionately(normal vs.pathological),this model was trained and tested.Results:Results using the proposed model reflect greater precision over traditional convolutional neural networks(CNNs)models,achieving a classification accuracy of 97.52%.Additionally,the model demonstrated exceptional performance in classification tasks.The ablation studies validated that all applications added value to the prediction process,particularly emphasizing the attention fusion module.Conclusion:The MPFAN-AF model demonstrates high efficiency together with interpretability because it shows promise as an integration solution for real-time mobile cataract detection screening systems.Standard performance indicators indicate that AI-based ophthalmology tools have a promising future for use in remote conditions that lack medical resources.
基金supported by theCONAHCYT(Consejo Nacional deHumanidades,Ciencias y Tecnologias).
文摘The use of Explainable Artificial Intelligence(XAI)models becomes increasingly important for making decisions in smart healthcare environments.It is to make sure that decisions are based on trustworthy algorithms and that healthcare workers understand the decisions made by these algorithms.These models can potentially enhance interpretability and explainability in decision-making processes that rely on artificial intelligence.Nevertheless,the intricate nature of the healthcare field necessitates the utilization of sophisticated models to classify cancer images.This research presents an advanced investigation of XAI models to classify cancer images.It describes the different levels of explainability and interpretability associated with XAI models and the challenges faced in deploying them in healthcare applications.In addition,this study proposes a novel framework for cancer image classification that incorporates XAI models with deep learning and advanced medical imaging techniques.The proposed model integrates several techniques,including end-to-end explainable evaluation,rule-based explanation,and useradaptive explanation.The proposed XAI reaches 97.72%accuracy,90.72%precision,93.72%recall,96.72%F1-score,9.55%FDR,9.66%FOR,and 91.18%DOR.It will discuss the potential applications of the proposed XAI models in the smart healthcare environment.It will help ensure trust and accountability in AI-based decisions,which is essential for achieving a safe and reliable smart healthcare environment.
基金funded by Researchers Supporting Program at King Saud University (RSPD2023R809).
文摘Pupil dynamics are the important characteristics of face spoofing detection.The face recognition system is one of the most used biometrics for authenticating individual identity.The main threats to the facial recognition system are different types of presentation attacks like print attacks,3D mask attacks,replay attacks,etc.The proposed model uses pupil characteristics for liveness detection during the authentication process.The pupillary light reflex is an involuntary reaction controlling the pupil’s diameter at different light intensities.The proposed framework consists of two-phase methodologies.In the first phase,the pupil’s diameter is calculated by applying stimulus(light)in one eye of the subject and calculating the constriction of the pupil size on both eyes in different video frames.The above measurement is converted into feature space using Kohn and Clynes model-defined parameters.The Support Vector Machine is used to classify legitimate subjects when the diameter change is normal(or when the eye is alive)or illegitimate subjects when there is no change or abnormal oscillations of pupil behavior due to the presence of printed photograph,video,or 3D mask of the subject in front of the camera.In the second phase,we perform the facial recognition process.Scale-invariant feature transform(SIFT)is used to find the features from the facial images,with each feature having a size of a 128-dimensional vector.These features are scale,rotation,and orientation invariant and are used for recognizing facial images.The brute force matching algorithm is used for matching features of two different images.The threshold value we considered is 0.08 for good matches.To analyze the performance of the framework,we tested our model in two Face antispoofing datasets named Replay attack datasets and CASIA-SURF datasets,which were used because they contain the videos of the subjects in each sample having three modalities(RGB,IR,Depth).The CASIA-SURF datasets showed an 89.9%Equal Error Rate,while the Replay Attack datasets showed a 92.1%Equal Error Rate.
文摘Purpose:The purpose of this work is to present an approach for autonomous detection of eye disease in fundus images.Furthermore,this work presents an improved variant of the Tiny YOLOv7 model developed specifically for eye disease detection.The model proposed in this work is a highly useful tool for the development of applications for autonomous detection of eye diseases in fundus images that can help and assist ophthalmologists.Design/methodology/approach:The approach adopted to carry out this work is twofold.Firstly,a richly annotated dataset consisting of eye disease classes,namely,cataract,glaucoma,retinal disease and normal eye,was created.Secondly,an improved variant of the Tiny YOLOv7 model was developed and proposed as EYE-YOLO.The proposed EYE-YOLO model has been developed by integrating multi-spatial pyramid pooling in the feature extraction network and Focal-EIOU loss in the detection network of the Tiny YOLOv7 model.Moreover,at run time,the mosaic augmentation strategy has been utilized with the proposed model to achieve benchmark results.Further,evaluations have been carried out for performance metrics,namely,precision,recall,F1 Score,average precision(AP)and mean average precision(mAP).Findings:The proposed EYE-YOLO achieved 28%higher precision,18%higher recall,24%higher F1 Score and 30.81%higher mAP than the Tiny YOLOv7 model.Moreover,in terms of AP for each class of the employed dataset,it achieved 9.74%higher AP for cataract,27.73%higher AP for glaucoma,72.50%higher AP for retina disease and 13.26%higher AP for normal eye.In comparison to the state-of-the-art Tiny YOLOv5,Tiny YOLOv6 and Tiny YOLOv8 models,the proposed EYE-YOLO achieved 6:23.32%higher mAP.Originality/value:This work addresses the problem of eye disease recognition as a bounding box regression and detection problem.Whereas,the work in the related research is largely based on eye disease classification.The other highlight of this work is to propose a richly annotated dataset for different eye diseases useful for training deep learning-based object detectors.The major highlight of this work lies in the proposal of an improved variant of the Tiny YOLOv7 model focusing on eye disease detection.The proposed modifications in the Tiny YOLOv7 aided the proposed model in achieving better results as compared to the state-of-the-art Tiny YOLOv8 and YOLOv8 Nano.
