Breast Cancer(BC)remains a leadingmalignancy among women,resulting in highmortality rates.Early and accurate detection is crucial for improving patient outcomes.Traditional diagnostic tools,while effective,have limita...Breast Cancer(BC)remains a leadingmalignancy among women,resulting in highmortality rates.Early and accurate detection is crucial for improving patient outcomes.Traditional diagnostic tools,while effective,have limitations that reduce their accessibility and accuracy.This study investigates the use ofConvolutionalNeuralNetworks(CNNs)to enhance the diagnostic process of BC histopathology.Utilizing the BreakHis dataset,which contains thousands of histopathological images,we developed a CNN model designed to improve the speed and accuracy of image analysis.Our CNN architecture was designed with multiple convolutional layers,max-pooling layers,and a fully connected network optimized for feature extraction and classification.Hyperparameter tuning was conducted to identify the optimal learning rate,batch size,and number of epochs,ensuring robust model performance.The dataset was divided into training(80%),validation(10%),and testing(10%)subsets,with performance evaluated using accuracy,precision,recall,and F1-score metrics.Our CNN model achieved a magnification-independent accuracy of 97.72%,with specific accuracies of 97.50%at 40×,97.61%at 100×,99.06%at 200×,and 97.25%at 400×magnification levels.These results demonstrate the model’s superior performance relative to existing methods.The integration of CNNs in diagnostic workflows can potentially reduce pathologist workload,minimize interpretation errors,and increase the availability of diagnostic testing,thereby improving BC management and patient survival rates.This study highlights the effectiveness of deep learning in automating BC histopathological classification and underscores the potential for AI-driven diagnostic solutions to improve patient care.展开更多
A heart attack disrupts the normal flow of blood to the heart muscle,potentially causing severe damage or death if not treated promptly.It can lead to long-term health complications,reduce quality of life,and signific...A heart attack disrupts the normal flow of blood to the heart muscle,potentially causing severe damage or death if not treated promptly.It can lead to long-term health complications,reduce quality of life,and significantly impact daily activities and overall well-being.Despite the growing popularity of deep learning,several drawbacks persist,such as complexity and the limitation of single-model learning.In this paper,we introduce a residual learning-based feature fusion technique to achieve high accuracy in differentiating abnormal cardiac rhythms heart sound.Combining MobileNet with DenseNet201 for feature fusion leverages MobileNet lightweight,efficient architecture with DenseNet201,dense connections,resulting in enhanced feature extraction and improved model performance with reduced computational cost.To further enhance the fusion,we employed residual learning to optimize the hierarchical features of heart abnormal sounds during training.The experimental results demonstrate that the proposed fusion method achieved an accuracy of 95.67%on the benchmark PhysioNet-2016 Spectrogram dataset.To further validate the performance,we applied it to the BreakHis dataset with a magnification level of 100X.The results indicate that the model maintains robust performance on the second dataset,achieving an accuracy of 96.55%.it highlights its consistent performance,making it a suitable for various applications.展开更多
乳腺癌是全球女性最常见的恶性肿瘤,对其发病机制和特性的深入研究对预防、早期筛查和治疗至关重要。本研究旨在通过提出一种新的因果发现注意力图神经网络(CA-GAT)模型,来提高乳腺癌图像分类的准确性和效率。首先,分析了现有图神经网络...乳腺癌是全球女性最常见的恶性肿瘤,对其发病机制和特性的深入研究对预防、早期筛查和治疗至关重要。本研究旨在通过提出一种新的因果发现注意力图神经网络(CA-GAT)模型,来提高乳腺癌图像分类的准确性和效率。首先,分析了现有图神经网络(GNN)在乳腺癌图像分类中的问题,特别是快捷特征与因果特征的混杂关系。为了解决这一问题,我们提出了CA-GAT模型,该模型通过三个主要部分:软掩模估计、混杂解开和因果干预,来强化因果特征与预测之间的因果关系。在BreaKHis数据集上进行实验,评估了CA-GAT模型的性能。实验结果表明,CA-GAT模型在乳腺癌图像分类任务中取得了93.8%的分类精度,显著优于其他传统GNN模型和深度学习模型。此外,通过Micro F1分数和Macro F1分数的比较,进一步证明了CA-GAT模型在分类准确性和类别间平衡性能上的优势。本研究提出的CA-GAT模型有效地提高了乳腺癌图像分类的准确性,为乳腺癌的诊断和治疗提供了一种新的工具。未来的工作将集中在进一步优化模型结构和探索更多实际应用场景。Breast cancer is the most common malignant tumor among women worldwide, and in-depth research on its pathogenesis and characteristics is crucial for prevention, early screening, and treatment. This study aims to improve the accuracy and efficiency of breast cancer image classification by proposing a novel Causal Discovery Attention Graph Neural Network (CA-GAT) model. The research begins with an analysis of the issues faced by existing Graph Neural Networks (GNNs) in breast cancer image classification, particularly the confounding relationship between shortcut features and causal features. To address this issue, the CA-GAT model is introduced, which reinforces the causal relationship between causal features and predictions through three main components: soft mask estimation, disentanglement of confounders, and causal intervention. Experiments were conducted on the BreaKHis dataset to evaluate the performance of the CA-GAT model. The results demonstrate that the CA-GAT model achieved a classification accuracy of 93.8% in breast cancer image classification tasks, significantly outperforming other traditional GNN models and deep learning models. Furthermore, comparisons of Micro F1 scores and Macro F1 scores further confirm the CA-GAT model’s advantages in classification accuracy and balanced performance across categories. The CA-GAT model proposed in this study effectively enhances the accuracy of breast cancer image classification, providing a new tool for the diagnosis and treatment of breast cancer. Future work will focus on further optimizing the model structure and exploring more practical application scenarios.展开更多
Breast cancer is considered an immense threat and one of the leading causes of mortality in females.It is curable only when detected at an early stage.A standard cancer diagnosis approach involves detection of cancer-...Breast cancer is considered an immense threat and one of the leading causes of mortality in females.It is curable only when detected at an early stage.A standard cancer diagnosis approach involves detection of cancer-related anomalies in tumour histopathology images.Detection depends on the accurate identification of the landmarks in the visual artefacts present in the slide images.Researchers are continuously striving to develop automatic machine-learning algorithms for processing medical images to assist in tumour detection.Nowadays,computerbased automated systems play an important role in cancer image analysis and help healthcare experts make rapid and correct inferences about the type of cancer.This study proposes an effective convolutional neural networkbased(CNN-based)model that exploits the transfer-learning technique for automatic image classification between malignant and benign tumour,using histopathology images.Resnet50 architecture has been trained on new dataset for feature extraction,and fully connected layers have been fine-tuned for achieving highest training,validation and test accuracies.The result illustrated state-of-the-art performance of the proposed model with highest training,validation and test accuracies as 99.70%,99.24%and 99.24%,respectively.Classification accuracy is increased by 0.66%and 0.2%when compared with similar recent studies on training and test data results.Average precision and F1 score have also improved,and receiver operating characteristic(RoC)area has been achieved to 99.1%.Thus,a reliable,accurate and consistent CNN model based on pre-built Resnet50 architecture has been developed.展开更多
Breast cancer is one of the malignancies that endanger women’s health all over the world.Considering that there is some noise and edge blurring in breast pathological images,it is easier to extract shallow features o...Breast cancer is one of the malignancies that endanger women’s health all over the world.Considering that there is some noise and edge blurring in breast pathological images,it is easier to extract shallow features of noise and redundant information when VGG16 network is used,which is affected by its relative shallow depth and small convolution kernel.To improve the pathological diagnosis of breast cancers,we propose a classification method for benign and malignant tumors in the breast pathological images which is based on feature concatenation of VGG16 network.First,in order to improve the problems of small dataset size and unbalanced data samples,the original BreakHis dataset is processed by data augmentation technologies,such as geometric transformation and color enhancement.Then,to reduce noise and edge blurring in breast pathological images,we perform bilateral filtering and denoising on the original dataset and sharpen the edge features by Sobel operator,which makes the extraction of shallow features by VGG16 model more accurate.Based on transfer learning,the network model trained with the expanded dataset is called VGG16-1,and another model trained with the image denoising and sharpening and mixed with the original dataset is called VGG16-2.The features extracted by VGG16-1 and VGG16-2 are concatenated,and then classified by support vector machine.The final experimental results show that the average accuracy is 98.44%,98.89%,98.30%and 97.47%,respectively,when the proposed method is tested with the breast pathological images of 40×,100×,200×and 400×on BreakHis dataset.展开更多
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2024-02-01096).
文摘Breast Cancer(BC)remains a leadingmalignancy among women,resulting in highmortality rates.Early and accurate detection is crucial for improving patient outcomes.Traditional diagnostic tools,while effective,have limitations that reduce their accessibility and accuracy.This study investigates the use ofConvolutionalNeuralNetworks(CNNs)to enhance the diagnostic process of BC histopathology.Utilizing the BreakHis dataset,which contains thousands of histopathological images,we developed a CNN model designed to improve the speed and accuracy of image analysis.Our CNN architecture was designed with multiple convolutional layers,max-pooling layers,and a fully connected network optimized for feature extraction and classification.Hyperparameter tuning was conducted to identify the optimal learning rate,batch size,and number of epochs,ensuring robust model performance.The dataset was divided into training(80%),validation(10%),and testing(10%)subsets,with performance evaluated using accuracy,precision,recall,and F1-score metrics.Our CNN model achieved a magnification-independent accuracy of 97.72%,with specific accuracies of 97.50%at 40×,97.61%at 100×,99.06%at 200×,and 97.25%at 400×magnification levels.These results demonstrate the model’s superior performance relative to existing methods.The integration of CNNs in diagnostic workflows can potentially reduce pathologist workload,minimize interpretation errors,and increase the availability of diagnostic testing,thereby improving BC management and patient survival rates.This study highlights the effectiveness of deep learning in automating BC histopathological classification and underscores the potential for AI-driven diagnostic solutions to improve patient care.
文摘A heart attack disrupts the normal flow of blood to the heart muscle,potentially causing severe damage or death if not treated promptly.It can lead to long-term health complications,reduce quality of life,and significantly impact daily activities and overall well-being.Despite the growing popularity of deep learning,several drawbacks persist,such as complexity and the limitation of single-model learning.In this paper,we introduce a residual learning-based feature fusion technique to achieve high accuracy in differentiating abnormal cardiac rhythms heart sound.Combining MobileNet with DenseNet201 for feature fusion leverages MobileNet lightweight,efficient architecture with DenseNet201,dense connections,resulting in enhanced feature extraction and improved model performance with reduced computational cost.To further enhance the fusion,we employed residual learning to optimize the hierarchical features of heart abnormal sounds during training.The experimental results demonstrate that the proposed fusion method achieved an accuracy of 95.67%on the benchmark PhysioNet-2016 Spectrogram dataset.To further validate the performance,we applied it to the BreakHis dataset with a magnification level of 100X.The results indicate that the model maintains robust performance on the second dataset,achieving an accuracy of 96.55%.it highlights its consistent performance,making it a suitable for various applications.
文摘乳腺癌是全球女性最常见的恶性肿瘤,对其发病机制和特性的深入研究对预防、早期筛查和治疗至关重要。本研究旨在通过提出一种新的因果发现注意力图神经网络(CA-GAT)模型,来提高乳腺癌图像分类的准确性和效率。首先,分析了现有图神经网络(GNN)在乳腺癌图像分类中的问题,特别是快捷特征与因果特征的混杂关系。为了解决这一问题,我们提出了CA-GAT模型,该模型通过三个主要部分:软掩模估计、混杂解开和因果干预,来强化因果特征与预测之间的因果关系。在BreaKHis数据集上进行实验,评估了CA-GAT模型的性能。实验结果表明,CA-GAT模型在乳腺癌图像分类任务中取得了93.8%的分类精度,显著优于其他传统GNN模型和深度学习模型。此外,通过Micro F1分数和Macro F1分数的比较,进一步证明了CA-GAT模型在分类准确性和类别间平衡性能上的优势。本研究提出的CA-GAT模型有效地提高了乳腺癌图像分类的准确性,为乳腺癌的诊断和治疗提供了一种新的工具。未来的工作将集中在进一步优化模型结构和探索更多实际应用场景。Breast cancer is the most common malignant tumor among women worldwide, and in-depth research on its pathogenesis and characteristics is crucial for prevention, early screening, and treatment. This study aims to improve the accuracy and efficiency of breast cancer image classification by proposing a novel Causal Discovery Attention Graph Neural Network (CA-GAT) model. The research begins with an analysis of the issues faced by existing Graph Neural Networks (GNNs) in breast cancer image classification, particularly the confounding relationship between shortcut features and causal features. To address this issue, the CA-GAT model is introduced, which reinforces the causal relationship between causal features and predictions through three main components: soft mask estimation, disentanglement of confounders, and causal intervention. Experiments were conducted on the BreaKHis dataset to evaluate the performance of the CA-GAT model. The results demonstrate that the CA-GAT model achieved a classification accuracy of 93.8% in breast cancer image classification tasks, significantly outperforming other traditional GNN models and deep learning models. Furthermore, comparisons of Micro F1 scores and Macro F1 scores further confirm the CA-GAT model’s advantages in classification accuracy and balanced performance across categories. The CA-GAT model proposed in this study effectively enhances the accuracy of breast cancer image classification, providing a new tool for the diagnosis and treatment of breast cancer. Future work will focus on further optimizing the model structure and exploring more practical application scenarios.
文摘Breast cancer is considered an immense threat and one of the leading causes of mortality in females.It is curable only when detected at an early stage.A standard cancer diagnosis approach involves detection of cancer-related anomalies in tumour histopathology images.Detection depends on the accurate identification of the landmarks in the visual artefacts present in the slide images.Researchers are continuously striving to develop automatic machine-learning algorithms for processing medical images to assist in tumour detection.Nowadays,computerbased automated systems play an important role in cancer image analysis and help healthcare experts make rapid and correct inferences about the type of cancer.This study proposes an effective convolutional neural networkbased(CNN-based)model that exploits the transfer-learning technique for automatic image classification between malignant and benign tumour,using histopathology images.Resnet50 architecture has been trained on new dataset for feature extraction,and fully connected layers have been fine-tuned for achieving highest training,validation and test accuracies.The result illustrated state-of-the-art performance of the proposed model with highest training,validation and test accuracies as 99.70%,99.24%and 99.24%,respectively.Classification accuracy is increased by 0.66%and 0.2%when compared with similar recent studies on training and test data results.Average precision and F1 score have also improved,and receiver operating characteristic(RoC)area has been achieved to 99.1%.Thus,a reliable,accurate and consistent CNN model based on pre-built Resnet50 architecture has been developed.
基金the National Natural Science Foundation of China(No.62006073)。
文摘Breast cancer is one of the malignancies that endanger women’s health all over the world.Considering that there is some noise and edge blurring in breast pathological images,it is easier to extract shallow features of noise and redundant information when VGG16 network is used,which is affected by its relative shallow depth and small convolution kernel.To improve the pathological diagnosis of breast cancers,we propose a classification method for benign and malignant tumors in the breast pathological images which is based on feature concatenation of VGG16 network.First,in order to improve the problems of small dataset size and unbalanced data samples,the original BreakHis dataset is processed by data augmentation technologies,such as geometric transformation and color enhancement.Then,to reduce noise and edge blurring in breast pathological images,we perform bilateral filtering and denoising on the original dataset and sharpen the edge features by Sobel operator,which makes the extraction of shallow features by VGG16 model more accurate.Based on transfer learning,the network model trained with the expanded dataset is called VGG16-1,and another model trained with the image denoising and sharpening and mixed with the original dataset is called VGG16-2.The features extracted by VGG16-1 and VGG16-2 are concatenated,and then classified by support vector machine.The final experimental results show that the average accuracy is 98.44%,98.89%,98.30%and 97.47%,respectively,when the proposed method is tested with the breast pathological images of 40×,100×,200×and 400×on BreakHis dataset.
