An Effective Two Way Classification of Breast Cancer Images: A Detailed Review

Improved Loss Function for Mass Segmentation in Mammography Images Using Density and Mass Size

Mass segmentation is one of the fundamental tasks used when identifying breast cancer due to the comprehensive information it provides, including the location, size, and border of the masses. Despite significant improvement in the performance of the task, certain properties of the data, such as pixel class imbalance and the diverse appearance and sizes of masses, remain challenging. Recently, there has been a surge in articles proposing to address pixel class imbalance through the formulation of the loss function. While demonstrating an enhancement in performance, they mostly fail to address the problem comprehensively. In this paper, we propose a new perspective on the calculation of the loss that enables the binary segmentation loss to incorporate the sample-level information and region-level losses in a hybrid loss setting. We propose two variations of the loss to include mass size and density in the loss calculation. Also, we introduce a single loss variant using the idea of utilizing mass size and density to enhance focal loss. We tested the proposed method on benchmark datasets: CBIS-DDSM and INbreast. Our approach outperformed the baseline and state-of-the-art methods on both datasets.

Developing the breast cancer risk prediction system using hybrid machine learning algorithms

BACKGROUND

Breast cancer (BC) is the most common cause of cancer-related deaths in women globally. Currently, many machine learning (ML)-based predictive models have been established to assist clinicians in decision making for the prediction of BC. However, preventing risk factor formation even with having healthy lifestyle behaviors or preventing disease at early stages can significantly lead to optimal population-wide BC health. Thus, we aimed to develop a prediction model by using a genetic algorithm (GA) incorporating several ML algorithms for the prediction and early warning of BC.

MATERIAL AND METHODS

The data of 3168 healthy individuals and 1742 patient case records in the BC Registry Database in Ayatollah Taleghani hospital, Abadan, Iran were analyzed. First, a modified hybrid GA was used to perform feature selection and optimization of selected features. Then, with the use of selected features, several ML algorithms were trained to predict BC. Afterward, the performance of each model was measured in terms of accuracy, precision, sensitivity, specificity, and receiver operating characteristic (ROC) curve metrics. Finally, a clinical decision support system based on the best model was developed.

RESULTS

After performing feature selection, age, consumption of dairy products, BC family history, breast biopsy, chest X-ray, hormone therapy, alcohol consumption, being overweight, having children, and education statuses were selected as the most important features for prediction of BC. The experimental results showed that the decision tree yielded a superior performance than other ML models, with values of 99.3%, 99.5%, 98.26% for accuracy, specificity, and sensitivity, respectively.

CONCLUSION

The developed predictive system can accurately identify persons who are at elevated risk for BC and can be used as an essential clinical screening tool for the early prevention of BC and serve as an important tool for developing preventive health strategies.

Multi-scale attention-based convolutional neural network for classification of breast masses in mammograms

PURPOSE

Breast cancer is the cancer with the highest incidence in women, and early detection can effectively improve the survival rate of patients. Mammography is an important method for physicians to screening breast cancer, but the diagnosis of mammograms by physicians depends largely on clinical practice experience. Studies have shown that using computer-aided diagnosis techniques can help doctors diagnose breast cancer.

METHODS

In this paper, the method of convolutional neural network is mainly used to classify benign and malignant breast masses in the mammograms. First, we use multi-scale residual networks and densely connected networks as backbone networks to extract the features of global image patches and local image patches. Second, we use the attention module named convolutional block attention module (CBAM) to improve the two feature extraction networks to enhance the network's feature expression ability. Finally, we fuse the features of multi-scale image patches to achieve the classification of benign and malignant breast masses.

RESULTS

In the digital database for screening mammography (DDSM) database, the accuracy, sensitivity, AUC value and corresponding standard deviation of our method are 0.9626 ± 0.0110, 0.9719 ± 0.0126, and 0.9576 ± 0.0064, respectively. Compared with the commonly used ResNet (AUC = 0.8823 ± 0.0112) and DenseNet (AUC = 0.9141 ± 0.0085), the performance of our method has improved. In addition, we also used the INbreast database to train and validate the proposed method. The accuracy, sensitivity, AUC and corresponding standard deviations are 0.9554 ± 0.0296, 0.9605 ± 0.0228, and 0.9468 ± 0.0085, respectively.

CONCLUSIONS

Compared with the previous work, our proposed method uses multi-scale image features, has better classification performance in breast mass patches classification tasks, and can effectively assist physicians in breast cancer diagnosis.

Artificial intelligence in ultrasound

Ultrasound (US), a flexible green imaging modality, is expanding globally as a first-line imaging technique in various clinical fields following with the continual emergence of advanced ultrasonic technologies and the well-established US-based digital health system. Actually, in US practice, qualified physicians should manually collect and visually evaluate images for the detection, identification and monitoring of diseases. The diagnostic performance is inevitably reduced due to the intrinsic property of high operator-dependence from US. In contrast, artificial intelligence (AI) excels at automatically recognizing complex patterns and providing quantitative assessment for imaging data, showing high potential to assist physicians in acquiring more accurate and reproducible results. In this article, we will provide a general understanding of AI, machine learning (ML) and deep learning (DL) technologies; We then review the rapidly growing applications of AI-especially DL technology in the field of US-based on the following anatomical regions: thyroid, breast, abdomen and pelvis, obstetrics heart and blood vessels, musculoskeletal system and other organs by covering image quality control, anatomy localization, object detection, lesion segmentation, and computer-aided diagnosis and prognosis evaluation; Finally, we offer our perspective on the challenges and opportunities for the clinical practice of biomedical AI systems in US.

Age Is Important for the Early-Stage Detection of Breast Cancer on Both Transcriptomic and Methylomic Biomarkers

Patients at different ages have different rates of cell development and metabolisms. As a result, age should be an essential part of how a disease diagnosis model is trained and optimized. Unfortunately, most of the existing studies have not taken age into account. This study demonstrated that disease diagnosis models could be improved by merely applying individual models for patients of different age groups. Both transcriptomes and methylomes of the TCGA breast cancer dataset (TCGA-BRCA) were utilized for the analysis procedure of feature selection and classification. Our experimental data strongly suggested that disease diagnosis modeling should integrate patient age into the whole experimental design.