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
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.
文摘目的探讨自回归移动平均模型-长短期记忆(autoregressive integrated moving average-long short-term memory,ARIMA-LSTM)组合模型在肾综合征出血热(hemorrhagic fever with renal syndrome,HFRS)不同流行模式发病率预测中应用的可行性。方法收集1961—2020年全国HFRS年发病率、2004年1月至2020年12月全国、黑龙江省、吉林省、辽宁省、陕西省、山东省、河北省、广东省HFRS逐月发病率数据;全国及黑龙江省作为冬峰较春峰高代表,吉林省、辽宁省作为春峰与冬峰相当代表,陕西省、山东省作为仅存在冬峰代表,河北省、广东省作为仅存在春峰代表。1961—2014年逐年发病率、2004年1月至2020年6月逐月发病率数据作为训练集,2015—2020年逐年发病率、2020年7-12月逐月发病率数据作为测试集。分别建立ARIMA模型、ARIMA-LSTM组合模型,采用平均绝对百分比误差下降率(decline rate of mean absolute percentage error,DR_(MAPE))、均方根误差下降率(decline rate of root mean squared error,DRRMSE)评价模型拟合及预测精度优化程度。结果全国逐年、全国及黑龙江省、吉林省、辽宁省、陕西省、山东省、河北省、广东省逐月HFRS发病率拟合最佳ARIMA模型分别为ARIMA(2,0,0)、ARIMA(3,1,0)(2,1,1)_(12)、ARIMA(2,0,1)(2,1,1)_(12)、ARIMA(3,0,0)(2,1,1)_(12)含常数项、ARIMA(2,1,1)(2,1,1)_(12)、ARIMA(1,0,3)(1,1,0)_(12)、ARIMA(0,1,3)(2,1,1)_(12)、ARIMA(1,1,3)(2,0,0)_(12)、ARIMA(3,1,1)(1,1,1)_(12)。全国逐年、全国及黑龙江省、吉林省、辽宁省、陕西省、山东省、河北省、广东省逐月数据建立ARIMA-LSTM组合模型较ARIMA模型拟合的DR_(MAPE)依次为-19.57%、-46.38%、-43.27%、-46.37%、-49.70%、-48.36%、-58.23%、-35.52%、-48.74%;DRRMSE依次为-11.21%、-36.17%、-64.89%、-55.68%、-54.81%、-31.76%、-39.69%、-55.64%、-30.06%。全国逐年、全国及黑龙江省、吉林省、辽宁省、陕西省、山东省、河北省、广东省逐月数据建立ARIMA-LSTM组合模型较ARIMA模型预测的DR_(MAPE)依次为-11.10%、-8.69%、-19.68%、-36.17%、-55.57%、-9.44%、-14.60%、-14.22%、-9.26%;DRRMSE依次为-14.43%、-7.42%、-12.66%、-13.83%、-36.56%、10.37%、81.14%、-19.68%、-1.18%。结论ARIMA-LSTM组合模型总体在各类HFRS数据中拟合及预测效果均优于ARIMA模型,LSTM适于我国HFRS预测模型优化,但陕西省和山东省不适于ARIMA-LSTM预测。
文摘针对不同磁密幅值、频率、谐波组合等复杂激励工况下磁致伸缩建模面临的精准性问题,该文利用空间注意力机制(spatial attention mechanism,SAM)对传统的卷积神经网络(convolutional neural network,CNN)进行改进,将SAM嵌套入CNN网络中,建立SAMCNN改进型网络。再结合双向长短期记忆(bidirectional long short-term memory,BiLSTM)网络,提出电工钢片SAMCNN-BiLSTM磁致伸缩模型。首先,利用灰狼优化算法(grey wolf optimization,GWO)寻优神经网络结构的参数,实现复杂工况下磁致伸缩效应的准确表征;然后,建立中低频范围单频与叠加谐波激励等复杂工况下的磁致伸缩应变数据库,开展数据预处理与特征分析;最后,对SAMCNN-BiLSTM模型开展对比验证。对比叠加3次谐波激励下的磁致伸缩应变频谱主要分量,SAMCNN-BiLSTM模型计算值最大相对误差为3.70%,其比Jiles-Atherton-Sablik(J-A-S)、二次畴转等模型能更精确地表征电工钢片的磁致伸缩效应。
