Breast density classification with deep convolutional neural networks

Breast density classification is an essential part of breast cancer screening. Although a lot of prior work considered this problem as a task for learning algorithms, to our knowledge, all of them used small and not clinically realistic data both for training and evaluation of their models. In this work, we explore the limits of this task with a data set coming from over 200,000 breast cancer screening exams. We use this data to train and evaluate a strong convolutional neural network classifier. In a reader study, we find that our model can perform this task comparably to a human expert

A Modified LeNet CNN for Breast Cancer Diagnosis in Ultrasound Images

Convolutional neural networks (CNNs) have been extensively utilized in medical image processing to automatically extract meaningful features and classify various medical conditions, enabling faster and more accurate diagnoses. In this paper, LeNet, a classic CNN architecture, has been successfully applied to breast cancer data analysis. It demonstrates its ability to extract discriminative features and classify malignant and benign tumors with high accuracy, thereby supporting early detection and diagnosis of breast cancer. LeNet with corrected Rectified Linear Unit (ReLU), a modification of the traditional ReLU activation function, has been found to improve the performance of LeNet in breast cancer data analysis tasks via addressing the “dying ReLU” problem and enhancing the discriminative power of the extracted features. This has led to more accurate, reliable breast cancer detection and diagnosis and improved patient outcomes. Batch normalization improves the performance and training stability of small and shallow CNN architecture like LeNet. It helps to mitigate the effects of internal covariate shift, which refers to the change in the distribution of network activations during training. This classifier will lessen the overfitting problem and reduce the running time. The designed classifier is evaluated against the benchmarking deep learning models, proving that this has produced a higher recognition rate. The accuracy of the breast image recognition rate is 89.91%. This model will achieve better performance in segmentation, feature extraction, classification, and breast cancer tumor detection

A Decision-Making Tool for Early Detection of Breast Cancer on Mammographic Images

Breast cancer is one of the most dangerous types of cancer in the world among females. In the medical industry, the early detection of a breast abnormality in a mammogram can significantly decrease the death rate caused by breast cancer. Therefore, researchers directed their focus and efforts to find better solutions. Whereas researchers earlier used semi-automatic algorithms of machine learning, recently the attention is redirected toward deep learning algorithms that automatically extract features. Therefore, in the research study, two pre-trained Convolutional Neural Network models, VGG16 and ResNet50, have been used and applied on mammogram images to classify their abnormalities in terms of (1) the Benign Calcification, (2) the Malignant Calcification, (3) the Benign Mass, and (4) the Malignant Mass. The mammographic images of the CBIS-DDSM dataset are used. In the training phase, various experiments are performed on ROI images to decide on the best model configuration and fine-tuning depth. The experimental results showed that the VGG16 model provided a remarkable advancement over the ResNet50 model; the accuracy obtained was 80.0% in the first model whereas the second model could classify with a 60.0% accuracy almost randomly. Apart from accuracy, the other performance metrics used in this study are precision, recall, F1-Score and AUC. Our evaluation, based on these performance metrics, shows that accurate detection effect is obtained from the two networks with VGG16 being the most accurate. Finally, a decision support tool is developed which classifies the full mammogram images based on the fine-tuned VGG16 architecture into Benign Calcification, Malignant Calcification, Benign Mass, and Malignant Mass

Comparative Study on Local Binary Patterns for Mammographic Density and Risk Scoring

Breast density is considered to be one of the major risk factors in developing breast cancer. High breast density can also affect the accuracy of mammographic abnormality detection due to the breast tissue characteristics and patterns. We reviewed variants of local binary pattern descriptors to classify breast tissue which are widely used as texture descriptors for local feature extraction. In our study, we compared the classification results for the variants of local binary patterns such as classic LBP (Local Binary Pattern), ELBP (Elliptical Local Binary Pattern), Uniform ELBP, LDP (Local Directional Pattern) and M-ELBP (Mean-ELBP). A wider comparison with alternative texture analysis techniques was studied to investigate the potential of LBP variants in density classification. In addition, we investigated the effect on classification when using descriptors for the fibroglandular disk region and the whole breast region. We also studied the effect of the Region-of-Interest (ROI) size and location, the descriptor size, and the choice of classifier. The classification results were evaluated based on the MIAS database using a ten-run ten-fold cross validation approach. The experimental results showed that the Elliptical Local Binary Pattern descriptors and Local Directional Patterns extracted most relevant features for mammographic tissue classification indicating the relevance of directional filters. Similarly, the study showed that classification of features from ROIs of the fibroglandular disk region performed better than classification based on the whole breast region