Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient manner.Identifying different types of brain tumors,including gliomas,meningiomas,p...Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient manner.Identifying different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI images.Current approaches predominantly rely on traditional machine learning and basic deep learning methods for image classification.These methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI images.Utilizing the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor classification.Our approach highlights a major advancement in employing sophisticated machine learning techniques within Computer Science and Engineering,showcasing a highly accurate framework with significant potential for healthcare technologies.The model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification report.This successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current methods.The integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider application.This research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.展开更多
Citrus fruit crops are among the world’s most important agricultural products,but pests and diseases impact their cultivation,resulting in yield and quality losses.Computer vision and machine learning have been widel...Citrus fruit crops are among the world’s most important agricultural products,but pests and diseases impact their cultivation,resulting in yield and quality losses.Computer vision and machine learning have been widely used to detect and classify plant diseases over the last decade,allowing for early disease detection and improving agricultural production.This paper presented an automatic system for the early detection and classification of citrus plant diseases based on a deep learning(DL)model,which improved accuracy while decreasing computational complexity.The most recent transfer learning-based models were applied to the Citrus Plant Dataset to improve classification accuracy.Using transfer learning,this study successfully proposed a Convolutional Neural Network(CNN)-based pre-trained model(EfficientNetB3,ResNet50,MobiNetV2,and InceptionV3)for the identification and categorization of citrus plant diseases.To evaluate the architecture’s performance,this study discovered that transferring an EfficientNetb3 model resulted in the highest training,validating,and testing accuracies,which were 99.43%,99.48%,and 99.58%,respectively.In identifying and categorizing citrus plant diseases,the proposed CNN model outperforms other cuttingedge CNN model architectures developed previously in the literature.展开更多
基金supported by the Researchers Supporting Program at King Saud University.Researchers Supporting Project number(RSPD2024R867),King Saud University,Riyadh,Saudi Arabia.
文摘Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient manner.Identifying different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI images.Current approaches predominantly rely on traditional machine learning and basic deep learning methods for image classification.These methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI images.Utilizing the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor classification.Our approach highlights a major advancement in employing sophisticated machine learning techniques within Computer Science and Engineering,showcasing a highly accurate framework with significant potential for healthcare technologies.The model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification report.This successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current methods.The integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider application.This research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.
基金supported by the“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning(KETEP)and granted financial resources from the Ministry of Trade,Industry,and Energy,Republic of Korea(No.20204010600090)The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Small Groups.Project under grant number(R.G.P.1/257/43).
文摘Citrus fruit crops are among the world’s most important agricultural products,but pests and diseases impact their cultivation,resulting in yield and quality losses.Computer vision and machine learning have been widely used to detect and classify plant diseases over the last decade,allowing for early disease detection and improving agricultural production.This paper presented an automatic system for the early detection and classification of citrus plant diseases based on a deep learning(DL)model,which improved accuracy while decreasing computational complexity.The most recent transfer learning-based models were applied to the Citrus Plant Dataset to improve classification accuracy.Using transfer learning,this study successfully proposed a Convolutional Neural Network(CNN)-based pre-trained model(EfficientNetB3,ResNet50,MobiNetV2,and InceptionV3)for the identification and categorization of citrus plant diseases.To evaluate the architecture’s performance,this study discovered that transferring an EfficientNetb3 model resulted in the highest training,validating,and testing accuracies,which were 99.43%,99.48%,and 99.58%,respectively.In identifying and categorizing citrus plant diseases,the proposed CNN model outperforms other cuttingedge CNN model architectures developed previously in the literature.
