Honeycombing Lung(HCL)is a chronic lung condition marked by advanced fibrosis,resulting in enlarged air spaces with thick fibrotic walls,which are visible on Computed Tomography(CT)scans.Differentiating between normal...Honeycombing Lung(HCL)is a chronic lung condition marked by advanced fibrosis,resulting in enlarged air spaces with thick fibrotic walls,which are visible on Computed Tomography(CT)scans.Differentiating between normal lung tissue,honeycombing lungs,and Ground Glass Opacity(GGO)in CT images is often challenging for radiologists and may lead to misinterpretations.Although earlier studies have proposed models to detect and classify HCL,many faced limitations such as high computational demands,lower accuracy,and difficulty distinguishing between HCL and GGO.CT images are highly effective for lung classification due to their high resolution,3D visualization,and sensitivity to tissue density variations.This study introduces Honeycombing Lungs Network(HCL Net),a novel classification algorithm inspired by ResNet50V2 and enhanced to overcome the shortcomings of previous approaches.HCL Net incorporates additional residual blocks,refined preprocessing techniques,and selective parameter tuning to improve classification performance.The dataset,sourced from the University Malaya Medical Centre(UMMC)and verified by expert radiologists,consists of CT images of normal,honeycombing,and GGO lungs.Experimental evaluations across five assessments demonstrated that HCL Net achieved an outstanding classification accuracy of approximately 99.97%.It also recorded strong performance in other metrics,achieving 93%precision,100%sensitivity,89%specificity,and an AUC-ROC score of 97%.Comparative analysis with baseline feature engineering methods confirmed the superior efficacy of HCL Net.The model significantly reduces misclassification,particularly between honeycombing and GGO lungs,enhancing diagnostic precision and reliability in lung image analysis.展开更多
Background: complex proximal tibial fractures (Types V & VI of Schautzker classification) are the major problems in orthopedic surgery and associated with high complication rates. There are many alternatives in tr...Background: complex proximal tibial fractures (Types V & VI of Schautzker classification) are the major problems in orthopedic surgery and associated with high complication rates. There are many alternatives in treatment of these fractures. Aim: to evaluate the results of double plating with single anterior incision in complex proximal tibial fractures (Types V& VI of Schautzker classification).Methods and Materials: 22 patients (16 males and 6 females) with Types V and VI of Schautzker classification of proximal tibial fractures (14 cases were Type V and 8 cases were Type VI) were treated by double plating with single anterior incision method between May 2006 and May 2011. The bony and functional outcome was evaluated according to Knee Society Score. Results: According to Knee Society Score, the results were as follows: excellent in 19 patients (86.4%), good in 2 patients (9.1%), fair in 1 patient (4/5%), and poor in no patient (0%).Conclusion: the double plate fixation with single anterior incision is the best, effective and simple procedure in treatment of complex proximal tibial fractures (Types V and VI of Schautzker classification).展开更多
Achieving reliable and efficient weather classification for autonomous vehicles is crucial for ensuring safety and operational effectiveness.However,accurately classifying diverse and complex weather conditions remain...Achieving reliable and efficient weather classification for autonomous vehicles is crucial for ensuring safety and operational effectiveness.However,accurately classifying diverse and complex weather conditions remains a significant challenge.While advanced techniques such as Vision Transformers have been developed,they face key limitations,including high computational costs and limited generalization across varying weather conditions.These challenges present a critical research gap,particularly in applications where scalable and efficient solutions are needed to handle weather phenomena’intricate and dynamic nature in real-time.To address this gap,we propose a Multi-level Knowledge Distillation(MLKD)framework,which leverages the complementary strengths of state-of-the-art pre-trained models to enhance classification performance while minimizing computational overhead.Specifically,we employ ResNet50V2 and EfficientNetV2B3 as teacher models,known for their ability to capture complex image features and distil their knowledge into a custom lightweight Convolutional Neural Network(CNN)student model.This framework balances the trade-off between high classification accuracy and efficient resource consumption,ensuring real-time applicability in autonomous systems.Our Response-based Multi-level Knowledge Distillation(R-MLKD)approach effectively transfers rich,high-level feature representations from the teacher models to the student model,allowing the student to perform robustly with significantly fewer parameters and lower computational demands.The proposed method was evaluated on three public datasets(DAWN,BDD100K,and CITS traffic alerts),each containing seven weather classes with 2000 samples per class.The results demonstrate the effectiveness of MLKD,achieving a 97.3%accuracy,which surpasses conventional deep learning models.This work improves classification accuracy and tackles the practical challenges of model complexity,resource consumption,and real-time deployment,offering a scalable solution for weather classification in autonomous driving systems.展开更多
Plant disease classification and prevention of spreading of the disease at earlier stages based on visual leaves symptoms and Pest recognition through deep learning-based image classification is in the forefront of re...Plant disease classification and prevention of spreading of the disease at earlier stages based on visual leaves symptoms and Pest recognition through deep learning-based image classification is in the forefront of research.To perform the investigation on Plant and pest classification,Transfer Learning(TL)approach is used on EfficientNet-V2.TL requires limited labelled data and shorter training time.However,the limitation of TL is the pre-trained model network’s topology is static and the knowledge acquired is detrimentally overwriting the old parameters.EfficientNet-V2 is a Convolutional Neural Network(CNN)model with significant high speed learning rates across variable sized datasets.The model employs a form of progressive learning mechanism which expands the network topology gradually over the course of training process improving the model’s learning capacity.This provides a better interpretability of the model’s understanding on the test domains.With these insights,our work investigates the effectiveness of EfficienetV2 model trained on a class imbalanced dataset for plant disease classification and pest recognition by means of combining TL and progressive learning approach.This Progressive Learning for TL(PL-TL)is used in our work consisting of 38 classes of PlantVillage dataset of crops and fruit species,5 classes of cassava leaf diseases and another dataset with around 102 classes of crop pest images downloaded from popular dataset platforms,though it is not a benchmark dataset.To test the predictability rate of the model in classifying leaf diseases with similar visual symptoms,Mix-up data augmentation technique is used at the ratio of 1:4 on corn and tomato classes which has high probability of misinterpretation of disease classes.Also,the paper compares the TL approach performed on the above mentioned three types of data set using well established CNN based Inceptionv3,and Vision Transformer a non-CNN model.It clearly depicts that EfficientNetV2 has an outstanding performance of 99.5%,97.5%,80.1%on Cassava,PlantVillage and IP102 datasets respectively at a faster rate irrespective of the data size and class distribution as compared to Inception-V3 and ViT models.The performance metrics in terms of accuracy,precision,f1-score is also studied.展开更多
文摘Honeycombing Lung(HCL)is a chronic lung condition marked by advanced fibrosis,resulting in enlarged air spaces with thick fibrotic walls,which are visible on Computed Tomography(CT)scans.Differentiating between normal lung tissue,honeycombing lungs,and Ground Glass Opacity(GGO)in CT images is often challenging for radiologists and may lead to misinterpretations.Although earlier studies have proposed models to detect and classify HCL,many faced limitations such as high computational demands,lower accuracy,and difficulty distinguishing between HCL and GGO.CT images are highly effective for lung classification due to their high resolution,3D visualization,and sensitivity to tissue density variations.This study introduces Honeycombing Lungs Network(HCL Net),a novel classification algorithm inspired by ResNet50V2 and enhanced to overcome the shortcomings of previous approaches.HCL Net incorporates additional residual blocks,refined preprocessing techniques,and selective parameter tuning to improve classification performance.The dataset,sourced from the University Malaya Medical Centre(UMMC)and verified by expert radiologists,consists of CT images of normal,honeycombing,and GGO lungs.Experimental evaluations across five assessments demonstrated that HCL Net achieved an outstanding classification accuracy of approximately 99.97%.It also recorded strong performance in other metrics,achieving 93%precision,100%sensitivity,89%specificity,and an AUC-ROC score of 97%.Comparative analysis with baseline feature engineering methods confirmed the superior efficacy of HCL Net.The model significantly reduces misclassification,particularly between honeycombing and GGO lungs,enhancing diagnostic precision and reliability in lung image analysis.
文摘Background: complex proximal tibial fractures (Types V & VI of Schautzker classification) are the major problems in orthopedic surgery and associated with high complication rates. There are many alternatives in treatment of these fractures. Aim: to evaluate the results of double plating with single anterior incision in complex proximal tibial fractures (Types V& VI of Schautzker classification).Methods and Materials: 22 patients (16 males and 6 females) with Types V and VI of Schautzker classification of proximal tibial fractures (14 cases were Type V and 8 cases were Type VI) were treated by double plating with single anterior incision method between May 2006 and May 2011. The bony and functional outcome was evaluated according to Knee Society Score. Results: According to Knee Society Score, the results were as follows: excellent in 19 patients (86.4%), good in 2 patients (9.1%), fair in 1 patient (4/5%), and poor in no patient (0%).Conclusion: the double plate fixation with single anterior incision is the best, effective and simple procedure in treatment of complex proximal tibial fractures (Types V and VI of Schautzker classification).
文摘Achieving reliable and efficient weather classification for autonomous vehicles is crucial for ensuring safety and operational effectiveness.However,accurately classifying diverse and complex weather conditions remains a significant challenge.While advanced techniques such as Vision Transformers have been developed,they face key limitations,including high computational costs and limited generalization across varying weather conditions.These challenges present a critical research gap,particularly in applications where scalable and efficient solutions are needed to handle weather phenomena’intricate and dynamic nature in real-time.To address this gap,we propose a Multi-level Knowledge Distillation(MLKD)framework,which leverages the complementary strengths of state-of-the-art pre-trained models to enhance classification performance while minimizing computational overhead.Specifically,we employ ResNet50V2 and EfficientNetV2B3 as teacher models,known for their ability to capture complex image features and distil their knowledge into a custom lightweight Convolutional Neural Network(CNN)student model.This framework balances the trade-off between high classification accuracy and efficient resource consumption,ensuring real-time applicability in autonomous systems.Our Response-based Multi-level Knowledge Distillation(R-MLKD)approach effectively transfers rich,high-level feature representations from the teacher models to the student model,allowing the student to perform robustly with significantly fewer parameters and lower computational demands.The proposed method was evaluated on three public datasets(DAWN,BDD100K,and CITS traffic alerts),each containing seven weather classes with 2000 samples per class.The results demonstrate the effectiveness of MLKD,achieving a 97.3%accuracy,which surpasses conventional deep learning models.This work improves classification accuracy and tackles the practical challenges of model complexity,resource consumption,and real-time deployment,offering a scalable solution for weather classification in autonomous driving systems.
文摘Plant disease classification and prevention of spreading of the disease at earlier stages based on visual leaves symptoms and Pest recognition through deep learning-based image classification is in the forefront of research.To perform the investigation on Plant and pest classification,Transfer Learning(TL)approach is used on EfficientNet-V2.TL requires limited labelled data and shorter training time.However,the limitation of TL is the pre-trained model network’s topology is static and the knowledge acquired is detrimentally overwriting the old parameters.EfficientNet-V2 is a Convolutional Neural Network(CNN)model with significant high speed learning rates across variable sized datasets.The model employs a form of progressive learning mechanism which expands the network topology gradually over the course of training process improving the model’s learning capacity.This provides a better interpretability of the model’s understanding on the test domains.With these insights,our work investigates the effectiveness of EfficienetV2 model trained on a class imbalanced dataset for plant disease classification and pest recognition by means of combining TL and progressive learning approach.This Progressive Learning for TL(PL-TL)is used in our work consisting of 38 classes of PlantVillage dataset of crops and fruit species,5 classes of cassava leaf diseases and another dataset with around 102 classes of crop pest images downloaded from popular dataset platforms,though it is not a benchmark dataset.To test the predictability rate of the model in classifying leaf diseases with similar visual symptoms,Mix-up data augmentation technique is used at the ratio of 1:4 on corn and tomato classes which has high probability of misinterpretation of disease classes.Also,the paper compares the TL approach performed on the above mentioned three types of data set using well established CNN based Inceptionv3,and Vision Transformer a non-CNN model.It clearly depicts that EfficientNetV2 has an outstanding performance of 99.5%,97.5%,80.1%on Cassava,PlantVillage and IP102 datasets respectively at a faster rate irrespective of the data size and class distribution as compared to Inception-V3 and ViT models.The performance metrics in terms of accuracy,precision,f1-score is also studied.
