Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a ...Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a Convolutional Neural Network(CNN)with a Bidirectional Long Short-Term Memory(BiLSTM)architecture,optimized using the Firefly Optimization algorithm(FO).The proposed CNN-BiLSTM-FO model is tailored for structured biomedical data,capturing both local patterns and sequential dependencies in diagnostic features,while the Firefly Algorithm fine-tunes key hyperparameters to maximize predictive performance.The approach is evaluated on two benchmark biomedical datasets:one comprising diagnostic data for bone cancer detection and another for identifying marrow cell abnormalities.Experimental results demonstrate that the proposed method outperforms standard deep learning models,including CNN,LSTM,BiLSTM,and CNN-LSTM hybrids,significantly.The CNNBiLSTM-FO model achieves an accuracy of 98.55%for bone cancer detection and 96.04%for marrow abnormality classification.The paper also presents a detailed complexity analysis of the proposed algorithm and compares its performance across multiple evaluation metrics such as precision,recall,F1-score,and AUC.The results confirm the effectiveness of the firefly-based optimization strategy in improving classification accuracy and model robustness.This work introduces a scalable and accurate diagnostic solution that holds strong potential for integration into intelligent clinical decision-support systems.展开更多
为了探讨CRF在抑郁症发生发展过程中的作用,对正常大鼠侧脑室慢性注射CRF21天并与慢性非预见性应激刺激21天建立的抑郁症模型大鼠进行比较。运用旷场行为实验(open-field)观察大鼠主动性活动能力,用Morris water Maze法,以训练期的逃避...为了探讨CRF在抑郁症发生发展过程中的作用,对正常大鼠侧脑室慢性注射CRF21天并与慢性非预见性应激刺激21天建立的抑郁症模型大鼠进行比较。运用旷场行为实验(open-field)观察大鼠主动性活动能力,用Morris water Maze法,以训练期的逃避潜伏期为指标检测大鼠空间学习记忆能力。采用HPLC-UV法测定血清皮质醇含量,RT-PCR法检测CRF及其受体mRNA的表达。结果显示:慢性应激21天建立的模型大鼠主动性活动和学习记忆能力均明显下降,血清皮质醇含量显著升高,CRF及其受体R1 mRNA的表达增加。大鼠侧脑室慢性注射CRF21天后,其体重增量、主动性活动和学习记忆能力与慢性应激模型大鼠一样均明显降低。这些工作证明了CRF在抑郁症的发生发展过程中发挥了至关重要的作用,慢性应激导致机体CRF分泌持续增加可能是抑郁症发病的主要原因。展开更多
文摘Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a Convolutional Neural Network(CNN)with a Bidirectional Long Short-Term Memory(BiLSTM)architecture,optimized using the Firefly Optimization algorithm(FO).The proposed CNN-BiLSTM-FO model is tailored for structured biomedical data,capturing both local patterns and sequential dependencies in diagnostic features,while the Firefly Algorithm fine-tunes key hyperparameters to maximize predictive performance.The approach is evaluated on two benchmark biomedical datasets:one comprising diagnostic data for bone cancer detection and another for identifying marrow cell abnormalities.Experimental results demonstrate that the proposed method outperforms standard deep learning models,including CNN,LSTM,BiLSTM,and CNN-LSTM hybrids,significantly.The CNNBiLSTM-FO model achieves an accuracy of 98.55%for bone cancer detection and 96.04%for marrow abnormality classification.The paper also presents a detailed complexity analysis of the proposed algorithm and compares its performance across multiple evaluation metrics such as precision,recall,F1-score,and AUC.The results confirm the effectiveness of the firefly-based optimization strategy in improving classification accuracy and model robustness.This work introduces a scalable and accurate diagnostic solution that holds strong potential for integration into intelligent clinical decision-support systems.
文摘针对现有的中文命名实体识别算法没有充分考虑实体识别任务的数据特征,存在中文样本数据的类别不平衡、训练数据中的噪声太大和每次模型生成数据的分布差异较大的问题,提出了一种以BERT-BiLSTM-CRF(Bidirectional Encoder Representations from Transformers-Bidirectional Long Short-Term Memory-Conditional Random Field)为基线改进的中文命名实体识别模型。首先在BERT-BiLSTM-CRF模型上结合P-Tuning v2技术,精确提取数据特征,然后使用3个损失函数包括聚焦损失(Focal Loss)、标签平滑(Label Smoothing)和KL Loss(Kullback-Leibler divergence loss)作为正则项参与损失计算。实验结果表明,改进的模型在Weibo、Resume和MSRA(Microsoft Research Asia)数据集上的F 1得分分别为71.13%、96.31%、95.90%,验证了所提算法具有更好的性能,并且在不同的下游任务中,所提算法易于与其他的神经网络结合与扩展。
文摘为了探讨CRF在抑郁症发生发展过程中的作用,对正常大鼠侧脑室慢性注射CRF21天并与慢性非预见性应激刺激21天建立的抑郁症模型大鼠进行比较。运用旷场行为实验(open-field)观察大鼠主动性活动能力,用Morris water Maze法,以训练期的逃避潜伏期为指标检测大鼠空间学习记忆能力。采用HPLC-UV法测定血清皮质醇含量,RT-PCR法检测CRF及其受体mRNA的表达。结果显示:慢性应激21天建立的模型大鼠主动性活动和学习记忆能力均明显下降,血清皮质醇含量显著升高,CRF及其受体R1 mRNA的表达增加。大鼠侧脑室慢性注射CRF21天后,其体重增量、主动性活动和学习记忆能力与慢性应激模型大鼠一样均明显降低。这些工作证明了CRF在抑郁症的发生发展过程中发挥了至关重要的作用,慢性应激导致机体CRF分泌持续增加可能是抑郁症发病的主要原因。
