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
In the treatment of 63 cases of depression, by bilateral Baihui (GV 20),Sishencong (Ex-HN 1), Juque (CV 14) and Neiguan (PC 6) as the main acupoints, based upon pattern identification, with bilateral Zhigou (TE 6), Zu...In the treatment of 63 cases of depression, by bilateral Baihui (GV 20),Sishencong (Ex-HN 1), Juque (CV 14) and Neiguan (PC 6) as the main acupoints, based upon pattern identification, with bilateral Zhigou (TE 6), Zusanli (ST 36), Yanglingquan (GB 34)and Taichong (LR 3) added for liver qi stagnation and spleen deficiency, with bilateral Hegu (LI 4), Xuehai (SP 10), Sanyinjiao (SP 6) and Taichong (LR 3) added for stagnation of liver blood, with bilateral Shenmen (HT 7), Zusanli (ST 36), Sanyinjiao (SP 6) and Taibai (SP 3)added for deficiency in both the heart and spleen, and with bilateral Sanyinjiao (SP 6), Taibai(SP 3), Taixi (KI 3) and Guanyuan (CV 4), by moxibustion on Guanyuan (CV4) and needling techniques on the rest acupoints, the results showed clinical cure in 21 cases, remarkable effect in 18 cases, improvement in 20 cases and failure in 4 cases, in the treatments from 13 times to 45 times, at the average treatments of 26 times.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.
文摘In the treatment of 63 cases of depression, by bilateral Baihui (GV 20),Sishencong (Ex-HN 1), Juque (CV 14) and Neiguan (PC 6) as the main acupoints, based upon pattern identification, with bilateral Zhigou (TE 6), Zusanli (ST 36), Yanglingquan (GB 34)and Taichong (LR 3) added for liver qi stagnation and spleen deficiency, with bilateral Hegu (LI 4), Xuehai (SP 10), Sanyinjiao (SP 6) and Taichong (LR 3) added for stagnation of liver blood, with bilateral Shenmen (HT 7), Zusanli (ST 36), Sanyinjiao (SP 6) and Taibai (SP 3)added for deficiency in both the heart and spleen, and with bilateral Sanyinjiao (SP 6), Taibai(SP 3), Taixi (KI 3) and Guanyuan (CV 4), by moxibustion on Guanyuan (CV4) and needling techniques on the rest acupoints, the results showed clinical cure in 21 cases, remarkable effect in 18 cases, improvement in 20 cases and failure in 4 cases, in the treatments from 13 times to 45 times, at the average treatments of 26 times.
