In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures.MRI is particularly effective for detecting soft tissue...In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures.MRI is particularly effective for detecting soft tissue anomalies.Traditionally,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of data.To address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI scans.This manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning methods.There are three stages for learning;in the first stage,the whole dataset is used to learn the features.In the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented dataset.This method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for analysis.Various hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning process.HWBA-dataset registers maximum classification performance.We evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.展开更多
1958—1999年,在广东省佛山市南海区西樵镇一带发现了众多记录了史前人类活动的石器地点,石器中包括双肩石器和细石器。迄今为止,“西樵山遗址”被认定是4—7 ka B P的大型新石器时期采石场和加工场。2011—2022年,笔者经多次地质遗迹...1958—1999年,在广东省佛山市南海区西樵镇一带发现了众多记录了史前人类活动的石器地点,石器中包括双肩石器和细石器。迄今为止,“西樵山遗址”被认定是4—7 ka B P的大型新石器时期采石场和加工场。2011—2022年,笔者经多次地质遗迹和地质环境调查,在西樵山东南麓富贤村北面发现了良好的第四纪地层剖面。地质探槽剖面测量和地质年代学研究表明:富贤地点存在2套原始沉积地层:上部为第四纪全新世沼泽相地层,AMS14C校正年龄为5052—5409 a B P;下部为第四纪晚更新世冲积-洪积相地层,AMS14C校正年龄为38420—40502 a B P,OSL (光释光)年龄为41.977—43.796 ka B P;在晚更新世地层中发现2层含旧石器层,下部A1层主要石器类型有较大型刮削器、尖刃器、舌型刃器及小型石片工具,如各类刮削器、锯齿刃器、凹缺器、石刀、使用石片、石核等,包括带铤斧型小石刀;上部A2层明显出现更多石刀类型且常常附带修背和修铤工作,其中一件用于生产细小长石片的原始楔形石核引人关注。据平均沉积速率计算,下部A1石器层年龄为46.511—47.325 ka B P,上部A2石器层年龄为41.977—42.167 ka B P;距今大于5 ka的全新世沉积物中的石制品数量虽少,但器物类型仍具有明显继承性与发展性特点。本文的发现更新并延伸了西樵山国家地质公园和“西樵山文化”的内涵,首次突破了珠江三角洲地区有确切年代的晚更新世旧石器遗存的纪录,追踪到大约40—50 ka现代人在华南沿海的足迹,揭示了同期石器工业的面貌及其文化内涵的发展特征和演变。研究表明,在MIS3间冰段相对湿热时期以及MIS2相对干冷阶段,富贤地点的古人类面临环境变化的挑战而开启了新的生计模式,这对于揭示现代人对全球和区域环境变化的响应与适应的科学问题具有重要意义。展开更多
文摘In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures.MRI is particularly effective for detecting soft tissue anomalies.Traditionally,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of data.To address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI scans.This manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning methods.There are three stages for learning;in the first stage,the whole dataset is used to learn the features.In the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented dataset.This method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for analysis.Various hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning process.HWBA-dataset registers maximum classification performance.We evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.
文摘1958—1999年,在广东省佛山市南海区西樵镇一带发现了众多记录了史前人类活动的石器地点,石器中包括双肩石器和细石器。迄今为止,“西樵山遗址”被认定是4—7 ka B P的大型新石器时期采石场和加工场。2011—2022年,笔者经多次地质遗迹和地质环境调查,在西樵山东南麓富贤村北面发现了良好的第四纪地层剖面。地质探槽剖面测量和地质年代学研究表明:富贤地点存在2套原始沉积地层:上部为第四纪全新世沼泽相地层,AMS14C校正年龄为5052—5409 a B P;下部为第四纪晚更新世冲积-洪积相地层,AMS14C校正年龄为38420—40502 a B P,OSL (光释光)年龄为41.977—43.796 ka B P;在晚更新世地层中发现2层含旧石器层,下部A1层主要石器类型有较大型刮削器、尖刃器、舌型刃器及小型石片工具,如各类刮削器、锯齿刃器、凹缺器、石刀、使用石片、石核等,包括带铤斧型小石刀;上部A2层明显出现更多石刀类型且常常附带修背和修铤工作,其中一件用于生产细小长石片的原始楔形石核引人关注。据平均沉积速率计算,下部A1石器层年龄为46.511—47.325 ka B P,上部A2石器层年龄为41.977—42.167 ka B P;距今大于5 ka的全新世沉积物中的石制品数量虽少,但器物类型仍具有明显继承性与发展性特点。本文的发现更新并延伸了西樵山国家地质公园和“西樵山文化”的内涵,首次突破了珠江三角洲地区有确切年代的晚更新世旧石器遗存的纪录,追踪到大约40—50 ka现代人在华南沿海的足迹,揭示了同期石器工业的面貌及其文化内涵的发展特征和演变。研究表明,在MIS3间冰段相对湿热时期以及MIS2相对干冷阶段,富贤地点的古人类面临环境变化的挑战而开启了新的生计模式,这对于揭示现代人对全球和区域环境变化的响应与适应的科学问题具有重要意义。
