Classification Of Breast Lesions Research Articles

Detection and classification of breast lesions with You Only Look Once version 5.

Aim: To explore the ability of You Only Look Once version 5 (YOLOv5) to detect and classify breast lesions on dynamic contrast-enhanced MRI. Methods: Four YOLOv5 submodels were examined. A total of 2124 and 2226 images of benign and malignant lesions were obtained, respectively. Precision, recall rate and mean average precision were used to evaluate model performance. Results: The precision (0.916) and mean average precision _0.5 (0.894) of YOLOv5s were higher than those of YOLOv5m (0.832, 0.794), YOLOv5l (0.843, 0.803) and YOLOv5x (0.854, 0.821). In the validation set, YOLOv5s required 1.1ms to detect lesions per image. Conclusion: YOLOv5s was the fastest and had the highest precision among the four YOLOv5 submodels for the detection and classification of breast lesions on dynamic contrast-enhanced MRI. It has a greater clinical application value.

Ultrasound classification of non-mass breast lesions following BI-RADS presents high positive predictive value.

To investigate the positive predictive value of ultrasound classification of non-mass breast lesions (NMLs) following breast imaging reporting and data system (BI-RADS), and enhance understanding of NMLs. Fifty-nine women with 59 ultrasound-detected breast NMLs were finally enrolled. The ultrasound (US) features of breast NMLs were analyzed; the incidence of malignant NMLs was calculated; the malignancy risk stratification of US for breast NMLs was established using BI-RADS. The incidence of malignant NMLs was 4.59% of all breast carcinoma. Non-ductal hypoechoic area, microcalcifications and posterior shadowing are the main US features of malignant NMLs, and there were significant differences between malignant and benign NMLs for microcalcifications and posterior shadowing. Taking BI-RADS 4B as a cutoff value, the sensitivity, specificity, area under the receiver operating characteristic curve (AUC), positive and negative predictive values, and odds ratio of the BI-RADS category were 82.98%,41.67%,0.62,84.78%,38.46% and 3.48, respectively. Stratifying the malignancy risk of breast NMLs using the BI-RADS the sensitivity and positive and predictive value are promising, but the likelihood of malignancy of malignant NMLs is underestimated, and that of benign NMLs is overestimated. The solution may be that to separate NMLs from breast masses and use different malignancy risk stratification protocols.

Artificial intelligence, BI-RADS evaluation and morphometry: A novel combination to diagnose breast cancer using ultrasonography, results from multi-center cohorts

PurposeTo develop and validate a machine learning (ML) model for the classification of breast lesions on ultrasound images. MethodIn the present study, three separate data cohorts containing 1288 breast lesions from three countries (Malaysia, Iran, and Turkey) were utilized for MLmodel development and external validation. The model was trained on ultrasound images of 725 breast lesions, and validation was done separately on the remaining data. An expert radiologist and a radiology resident classified the lesions based on the BI-RADS lexicon. Thirteen morphometric features were selected from a contour of the lesion and underwent a three-step feature selection process. Five features were chosen to be fed into the model separately and combined with the imaging signs mentioned in the BI-RADS reference guide. A support vector classifier was trained and optimized. ResultsThe diagnostic profile of the model with various input data was compared to the expert radiologist and radiology resident. The agreement of each approach with histopathologic specimens was also determined. Based on BI-RADS and morphometric features, the model achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.885, which is higher than the expert radiologist and radiology resident performances with AUC of 0.814 and 0.632, respectively in all cohorts. DeLong’s test also showed that the AUC of the ML protocol was significantly different from that of the expert radiologist (ΔAUCs = 0.071, 95%CI: (0.056, 0.086), P = 0.005). ConclusionsThese results support the possible role of morphometric features in enhancing the already well-excepted classification schemes.

Combining deep-wavelet neural networks and support-vector machines to classify breast lesions in thermography images

Breast cancer is among the leading causes of cancer death among women. The occurrence of breast cancer is similar both in developed countries and in underdeveloped and developing nations, although mortality is higher in underdeveloped countries due to late detection. Even though mammography is the most used technique for the differential diagnosis of breast cancer, breast thermography can be used as a complementary technique, more accurate than self-examination but accurate enough to guide the use of a mammogram. However, thermal imaging is still difficult for radiologists to understand. Machine learning can help improve this scenario. Deep Wavelet Neural Networks are convolutional neural networks that do not necessarily learn, as they may have predefined filter banks as their neurons. In this work, we propose a deep hybrid architecture to support breast thermography imaging diagnosis based on five-layer Deep-Wavelet Neural Networks, to extract attributes of regions of interest from mammograms, and linear kernel support vector machines for final classification. Classical classifiers such as Bayesian classifiers, single hidden layer multilayer perceptrons, decision trees, Random Forests, and support vector machines were tested. The results showed that it is possible to detect and classify injuries with an average accuracy of 99% and an average kappa of 0.99, employing a 5-layer deep-wavelet network and a linear kernel support vector machine as the final classifier. Using a deep neural network with prefixed weights from the Wavelets Transform filter bank, it was possible to extract attributes and thus take the problem to a universe where it can be solved with relatively simple decision boundaries like those composed by linear kernel support vector machines. This shows that these new deep networks can be important in building complete solutions to improve breast thermography imaging results to support clinical diagnosis.

Spiking Neural Network Based on Multi-Scale Saliency Fusion for Breast Cancer Detection

Deep neural networks have been successfully applied in the field of image recognition and object detection, and the recognition results are close to or even superior to those from human beings. A deep neural network takes the activation function as the basic unit. It is inferior to the spiking neural network, which takes the spiking neuron model as the basic unit in the aspect of biological interpretability. The spiking neural network is considered as the third-generation artificial neural network, which is event-driven and has low power consumption. It modulates the process of nerve cells from receiving a stimulus to firing spikes. However, it is difficult to train spiking neural network directly due to the non-differentiable spiking neurons. In particular, it is impossible to train a spiking neural network using the back-propagation algorithm directly. Therefore, the application scenarios of spiking neural network are not as extensive as deep neural network, and a spiking neural network is mostly used in simple image classification tasks. This paper proposed a spiking neural network method for the field of object detection based on medical images using the method of converting a deep neural network to spiking neural network. The detection framework relies on the YOLO structure and uses the feature pyramid structure to obtain the multi-scale features of the image. By fusing the high resolution of low-level features and the strong semantic information of high-level features, the detection precision of the network is improved. The proposed method is applied to detect the location and classification of breast lesions with ultrasound and X-ray datasets, and the results are 90.67% and 92.81%, respectively.

Breast cancer detection and classification in mammogram using a three-stage deep learning framework based on PAA algorithm

In recent years, deep learning has been used to develop an automatic breast cancer detection and classification tool to assist doctors. In this paper, we proposed a three-stage deep learning framework based on an anchor-free object detection algorithm, named the Probabilistic Anchor Assignment (PAA) to improve diagnosis performance by automatically detecting breast lesions (i.e., mass and calcification) and further classifying mammograms into benign or malignant. Firstly, a single-stage PAA-based detector roundly finds suspicious breast lesions in mammogram. Secondly, we designed a two-branch ROI detector to further classify and regress these lesions that aim to reduce the number of false positives. Besides, in this stage, we introduced a threshold-adaptive post-processing algorithm with dense breast information. Finally, the benign or malignant lesions would be classified by an ROI classifier which combines local-ROI features and global-image features. In addition, considering the strong correlation between the task of detection head of PAA and the task of whole mammogram classification, we added an image classifier that utilizes the same global-image features to perform image classification. The image classifier and the ROI classifier jointly guide to enhance the feature extraction ability and further improve the performance of classification. We integrated three public datasets of mammograms (CBIS-DDSM, INbreast, MIAS) to train and test our model and compared our framework with recent state-of-the-art methods. The results show that our proposed method can improve the diagnostic efficiency of radiologists by automatically detecting and classifying breast lesions and classifying benign and malignant mammograms.

Improving Performance of Breast Lesion Classification Using a ResNet50 Model Optimized with a Novel Attention Mechanism.

Background: The accurate classification between malignant and benign breast lesions detected on mammograms is a crucial but difficult challenge for reducing false-positive recall rates and improving the efficacy of breast cancer screening. Objective: This study aims to optimize a new deep transfer learning model by implementing a novel attention mechanism in order to improve the accuracy of breast lesion classification. Methods: ResNet50 is selected as the base model to develop a new deep transfer learning model. To enhance the accuracy of breast lesion classification, we propose adding a convolutional block attention module (CBAM) to the standard ResNet50 model and optimizing a new model for this task. We assembled a large dataset with 4280 mammograms depicting suspicious soft-tissue mass-type lesions. A region of interest (ROI) is extracted from each image based on lesion center. Among them, 2480 and 1800 ROIs depict verified benign and malignant lesions, respectively. The image dataset is randomly split into two subsets with a ratio of 9:1 five times to train and test two ResNet50 models with and without using CBAM. Results: Using the area under ROC curve (AUC) as an evaluation index, the new CBAM-based ResNet50 model yields AUC = 0.866 ± 0.015, which is significantly higher than that obtained by the standard ResNet50 model (AUC = 0.772 ± 0.008) (p < 0.01). Conclusion: This study demonstrates that although deep transfer learning technology attracted broad research interest in medical-imaging informatic fields, adding a new attention mechanism to optimize deep transfer learning models for specific application tasks can play an important role in further improving model performances.

A deep learning approach for automatic detection, segmentation and classification of breast lesions from thermal images

PurposeThis study investigates implementation of deep learning (DL) approaches to breast tumor recognition based on thermal images. We propose to utilize Mask R-CNN technique on images by first assigning bounding boxes and then creating a border for each tumor volume to differentiate it from adjacent tissues and structures. In this manner, thermal images can be handled by a single DL model to successfully perform detection, classification, and segmentation of normal and abnormal breast tissues. MethodsThis study employs Mask R-CNN technique along with transfer learning models to accurately delineate breast volumes from adjacent tissues and structures. It is a novel study that uses a single DL model to carry out three steps of breast tumor diagnosis, namely detection, segmentation and classification of normal and abnormal tissues based on thermal images. ResultsTwo network architectures (ResNet-50 and ResNet-101) were trained for 60 epochs and were then evaluated based on their classification and segmentation performances. The testing process resulted in higher classification success for ResNet-50 backbone pretrained on COCO images (%97.1 accuracy). Detection and segmentation performances of this model were also higher with mAP of 0.921 and overlap score of 0.868. ConclusionsOur results indicate that the classification and segmentation performances of Mask R-CNN method on ResNet-50 architecture are better than the data reported in the literature for thermal breast image studies. It should be emphasized that our approach employs a single DL model that successfully performs detection, classification, and segmentation procedures for diagnosing normal and abnormal breast tissues.

Artificial Intelligence in Breast Ultrasound: The Emerging Future of Modern Medicine.

In today's world, progressively enormous popularity prevails around artificial intelligence (AI). AI is gaining popularity in the identification of various images. Therefore, it has been widely used in the ultrasound of the breast. Furthermore, AI can perform a quantitative evaluation, which further helps maintain the diagnosis's accuracy. Moreover, breast cancer is the most common cancer in women, posing a severe threat to women's health. Hence, its early detection is usually associated with a patient's prognosis. As a result, using AI in breast cancer screening and detection is highly crucial. The concept of AI in the perspective of breast ultrasound has been highlighted in this brief review article. It tends to focus on early AI, i.e., traditional machine learning and deep learning algorithms. Also, the use of AI in ultrasound and the use of it in mammography, magnetic resonance imaging, nuclear medicine imaging, and classification of breast lesions is broadly explained, along with the challenges faced in bringing AI into daily practice.

Natural Language Processing Approaches for Automated Multilevel and Multiclass Classification of Breast Lesions on Free-Text Cytopathology Reports.

The extensive growth and use of electronic health records (EHRs) and extending medical literature have led to huge opportunities to automate the extraction of relevant clinical information that helps in concise and effective clinical decision support. However, processing such information has traditionally been dependent on labor-intensive processes with human errors such as fatigue, oversight, and interobserver variability. Hence, this study aims at the processing of EHRs and performing multilevel and multiclass classification by fetching dominant characteristic features that are sufficient to detect and differentiate various types of breast lesions. In this study, unstructured EHRs on breast lesions obtained through fine-needle aspiration cytology technique are considered. The raw text was normalized into structured tabular form and converted to scores by performing sentiment analysis that helps to decide the total polarity or class label of the EHR. Supervised machine learning approaches, namely random forest and feed-forward neural network trained using Levenberg-Marquardt training function, are used for classification of the collected EHR data set containing 2,879 records that are split in the ratio of 80:20 as training and testing data sets, respectively. Random forest and feed-forward neural network classifiers gave the best performance with an accuracy of 99.36%, an overall receiver operating characteristic-area under the curve of 99.2%, a correlation with ground truth of 98.3%, and a histopathologic correlation of 98.6%. Natural language processing has huge potential to automate the extraction of clinical features from breast lesions. The proposed multilevel and multiclass classification approach is used to classify 13 different types of breast lesions with 20 different labels into five classes to decide the type of treatment that should be given to patients by a physician or oncologist.

Breast lesion detection using an anchor-free network from ultrasound images with segmentation-based enhancement

The survival rate of breast cancer patients is closely related to the pathological stage of cancer. The earlier the pathological stage, the higher the survival rate. Breast ultrasound is a commonly used breast cancer screening or diagnosis method, with simple operation, no ionizing radiation, and real-time imaging. However, ultrasound also has the disadvantages of high noise, strong artifacts, low contrast between tissue structures, which affect the effective screening of breast cancer. Therefore, we propose a deep learning based breast ultrasound detection system to assist doctors in the diagnosis of breast cancer. The system implements the automatic localization of breast cancer lesions and the diagnosis of benign and malignant lesions. The method consists of two steps: 1. Contrast enhancement of breast ultrasound images using segmentation-based enhancement methods. 2. An anchor-free network was used to detect and classify breast lesions. Our proposed method achieves a mean average precision (mAP) of 0.902 on the datasets used in our experiment. In detecting benign and malignant tumors, precision is 0.917 and 0.888, and recall is 0.980 and 0.963, respectively. Our proposed method outperforms other image enhancement methods and an anchor-based detection method. We propose a breast ultrasound image detection system for breast cancer detection. The system can locate and diagnose benign and malignant breast lesions. The test results on single dataset and mixed dataset show that the proposed method has good performance.

Fully-automated deep learning pipeline for segmentation and classification of breast ultrasound images

Breast cancer is the most prevalent type of cancer among the female world population. Its early detection has a crucial role in enhancing the effectiveness of treatments, as well as reducing serious complications and deaths. Ultrasound imaging represents a standard diagnostic technique for this purpose, due to its low invasiveness and cost. However, as this technique is susceptible to a certain degree of uncertainty, computer-aided solutions have been proposed in recent years to reduce the operator workload and improve the accuracy of diagnoses. Following this trend, the present study aims to design and propose a fully-automated and multi-layer pipeline for the segmentation and classification of breast lesions associated with cancer risk, from ultrasound images. To achieve this goal, we first evaluate and compare the performance of several Convolutional Neural Network (CNN) architectures in tackling the above tasks. Then, we combine the performance of such networks through specialized ensembles, to better discriminate among heterogeneous cases. Lastly, we present a novel step of cyclic mutual optimization that exploits the intermediate results of the classification step to improve the segmentation outcome and vice versa, in an iterative manner. Experimental findings obtained on public datasets show the superiority of the ensemble methods over the individual networks. Moreover, with a Dice coefficient of ∼82% in the segmentation task and an accuracy of ∼91% in the classification task, our best configuration also shows to be competitive with respect to the state-of-the-art.

A Hybrid Deep Transfer Learning of CNN-Based LR-PCA for Breast Lesion Diagnosis via Medical Breast Mammograms.

One of the most promising research areas in the healthcare industry and the scientific community is focusing on the AI-based applications for real medical challenges such as the building of computer-aided diagnosis (CAD) systems for breast cancer. Transfer learning is one of the recent emerging AI-based techniques that allow rapid learning progress and improve medical imaging diagnosis performance. Although deep learning classification for breast cancer has been widely covered, certain obstacles still remain to investigate the independency among the extracted high-level deep features. This work tackles two challenges that still exist when designing effective CAD systems for breast lesion classification from mammograms. The first challenge is to enrich the input information of the deep learning models by generating pseudo-colored images instead of only using the input original grayscale images. To achieve this goal two different image preprocessing techniques are parallel used: contrast-limited adaptive histogram equalization (CLAHE) and Pixel-wise intensity adjustment. The original image is preserved in the first channel, while the other two channels receive the processed images, respectively. The generated three-channel pseudo-colored images are fed directly into the input layer of the backbone CNNs to generate more powerful high-level deep features. The second challenge is to overcome the multicollinearity problem that occurs among the high correlated deep features generated from deep learning models. A new hybrid processing technique based on Logistic Regression (LR) as well as Principal Components Analysis (PCA) is presented and called LR-PCA. Such a process helps to select the significant principal components (PCs) to further use them for the classification purpose. The proposed CAD system has been examined using two different public benchmark datasets which are INbreast and mini-MAIS. The proposed CAD system could achieve the highest performance accuracies of 98.60% and 98.80% using INbreast and mini-MAIS datasets, respectively. Such a CAD system seems to be useful and reliable for breast cancer diagnosis.

Shape-Based Breast Lesion Classification Using Digital Tomosynthesis Images: The Role of Explainable Artificial Intelligence

Computer-aided diagnosis (CAD) systems can help radiologists in numerous medical tasks including classification and staging of the various diseases. The 3D tomosynthesis imaging technique adds value to the CAD systems in diagnosis and classification of the breast lesions. Several convolutional neural network (CNN) architectures have been proposed to classify the lesion shapes to the respective classes using a similar imaging method. However, not only is the black box nature of these CNN models questionable in the healthcare domain, but so is the morphological-based cancer classification, concerning the clinicians. As a result, this study proposes both a mathematically and visually explainable deep-learning-driven multiclass shape-based classification framework for the tomosynthesis breast lesion images. In this study, authors exploit eight pretrained CNN architectures for the classification task on the previously extracted regions of interests images containing the lesions. Additionally, the study also unleashes the black box nature of the deep learning models using two well-known perceptive explainable artificial intelligence (XAI) algorithms including Grad-CAM and LIME. Moreover, two mathematical-structure-based interpretability techniques, i.e., t-SNE and UMAP, are employed to investigate the pretrained models’ behavior towards multiclass feature clustering. The experimental results of the classification task validate the applicability of the proposed framework by yielding the mean area under the curve of 98.2%. The explanability study validates the applicability of all employed methods, mainly emphasizing the pros and cons of both Grad-CAM and LIME methods that can provide useful insights towards explainable CAD systems.

A Comparison of Computer-Aided Diagnosis Schemes Optimized Using Radiomics and Deep Transfer Learning Methods.

Objective: Radiomics and deep transfer learning are two popular technologies used to develop computer-aided detection and diagnosis (CAD) schemes of medical images. This study aims to investigate and to compare the advantages and the potential limitations of applying these two technologies in developing CAD schemes. Methods: A relatively large and diverse retrospective dataset including 3000 digital mammograms was assembled in which 1496 images depicted malignant lesions and 1504 images depicted benign lesions. Two CAD schemes were developed to classify breast lesions. The first scheme was developed using four steps namely, applying an adaptive multi-layer topographic region growing algorithm to segment lesions, computing initial radiomics features, applying a principal component algorithm to generate an optimal feature vector, and building a support vector machine classifier. The second CAD scheme was built based on a pre-trained residual net architecture (ResNet50) as a transfer learning model to classify breast lesions. Both CAD schemes were trained and tested using a 10-fold cross-validation method. Several score fusion methods were also investigated to classify breast lesions. CAD performances were evaluated and compared by the areas under the ROC curve (AUC). Results: The ResNet50 model-based CAD scheme yielded AUC = 0.85 ± 0.02, which was significantly higher than the radiomics feature-based CAD scheme with AUC = 0.77 ± 0.02 (p < 0.01). Additionally, the fusion of classification scores generated by the two CAD schemes did not further improve classification performance. Conclusion: This study demonstrates that using deep transfer learning is more efficient to develop CAD schemes and it enables a higher lesion classification performance than CAD schemes developed using radiomics-based technology.

Meta-Heuristic Algorithm-Tuned Neural Network for Breast Cancer Diagnosis Using Ultrasound Images.

Breast cancer is the most menacing cancer among all types of cancer in women around the globe. Early diagnosis is the only way to increase the treatment options which then decreases the death rate and increases the chance of survival in patients. However, it is a challenging task to differentiate abnormal breast tissues from normal tissues because of their structure and unclear boundaries. Therefore, early and accurate diagnosis and classification of breast lesions into malignant or benign lesions is an active domain of research. Over the decade, numerous artificial neural network (ANN)-based techniques were adopted in order to diagnose and classify breast cancer due to the unique characteristics of learning key features from complex data via a training process. However, these schemes have limitations like slow convergence and longer training time. To address the above mentioned issues, this paper employs a meta-heuristic algorithm for tuning the parameters of the neural network. The main novelty of this work is the computer-aided diagnosis scheme for detecting abnormalities in breast ultrasound images by integrating a wavelet neural network (WNN) and the grey wolf optimization (GWO) algorithm. Here, breast ultrasound (US) images are preprocessed with a sigmoid filter followed by interference-based despeckling and then by anisotropic diffusion. The automatic segmentation algorithm is adopted to extract the region of interest, and subsequently morphological and texture features are computed. Finally, the GWO-tuned WNN is exploited to accomplish the classification task. The classification performance of the proposed scheme is validated on 346 ultrasound images. Efficiency of the proposed methodology is evaluated by computing the confusion matrix and receiver operating characteristic (ROC) curve. Numerical analysis revealed that the proposed work can yield higher classification accuracy when compared to the prevailing methods and thereby proves its potential in effective breast tumor detection and classification. The proposed GWO-WNN method (98%) gives better accuracy than other methods like SOM-SVM (87.5), LOFA-SVM (93.62%), MBA-RF (96.85%), and BAS-BPNN (96.3%)

YOLO-LOGO: A transformer-based YOLO segmentation model for breast mass detection and segmentation in digital mammograms

Background and objectiveBoth mass detection and segmentation in digital mammograms play a crucial role in early breast cancer detection and treatment. Furthermore, clinical experience has shown that they are the upstream tasks of pathological classification of breast lesions. Recent advancements in deep learning have made the analyses faster and more accurate. This study aims to develop a deep learning model architecture for breast cancer mass detection and segmentation using the mammography. MethodsIn this work we proposed a double shot model for mass detection and segmentation simultaneously using a combination of YOLO (You Only Look Once) and LOGO (Local-Global) architectures. Firstly, we adopted YoloV5L6, the state-of-the-art object detection model, to position and crop the breast mass in mammograms with a high resolution; Secondly, to balance training efficiency and segmentation performance, we modified the LOGO training strategy to train the whole images and cropped images on the global and local transformer branches separately. The two branches were then merged to form the final segmentation decision. ResultsThe proposed YOLO-LOGO model was tested on two independent mammography datasets (CBIS-DDSM and INBreast). The proposed model performs significantly better than previous works. It achieves true positive rate 95.7% and mean average precision 65.0% for mass detection on CBIS-DDSM dataset. Its performance for mass segmentation on CBIS-DDSM dataset is F1-score=74.5% and IoU=64.0%. The similar performance trend is observed in another independent dataset INBreast as well. ConclusionsThe proposed model has a higher efficiency and better performance, reduces computational requirements, and improves the versatility and accuracy of computer-aided breast cancer diagnosis. Hence it has the potential to enable more assistance for doctors in early breast cancer detection and treatment, thereby reducing mortality.

Early detection and classification of abnormality in prior mammograms using image-to-image translation and YOLO techniques

Background and ObjectiveComputer-aided-detection (CAD) systems have been developed to assist radiologists on finding suspicious lesions in mammogram. Deep Learning technology have recently succeeded to increase the chance of recognizing abnormality at an early stage in order to avoid unnecessary biopsies and decrease the mortality rate. In this study, we investigated the effectiveness of an end-to-end fusion model based on You-Only-Look-Once (YOLO) architecture, to simultaneously detect and classify suspicious breast lesions on digital mammograms. Four categories of cases were included: Mass, Calcification, Architectural Distortions, and Normal from a private digital mammographic database including 413 cases. For all cases, Prior mammograms (typically scanned 1 year before) were all reported as Normal, while Current mammograms were diagnosed as cancerous (confirmed by biopsies) or healthy. MethodsWe propose to apply the YOLO-based fusion model to the Current mammograms for breast lesions detection and classification. Then apply the same model retrospectively to synthetic mammograms for an early cancer prediction, where the synthetic mammograms were generated from the Prior mammograms by using the image-to-image translation models, CycleGAN and Pix2Pix. ResultsEvaluation results showed that our methodology could significantly detect and classify breast lesions on Current mammograms with a highest rate of 93% ± 0.118 for Mass lesions, 88% ± 0.09 for Calcification lesions, and 95% ± 0.06 for Architectural Distortion lesions. In addition, we reported evaluation results on Prior mammograms with a highest rate of 36% ± 0.01 for Mass lesions, 14% ± 0.01 for Calcification lesions, and 50% ± 0.02 for Architectural Distortion lesions. Normal mammograms were accordingly classified with an accuracy rate of 92% ± 0.09 and 90% ± 0.06 respectively on Current and Prior exams. ConclusionsOur proposed framework was first developed to help detecting and identifying suspicious breast lesions in X-ray mammograms on their Current screening. The work was also suggested to reduce the temporal changes between pairs of Prior and follow-up screenings for early predicting the location and type of abnormalities in Prior mammogram screening. The paper presented a CAD method to assist doctors and experts to identify the risk of breast cancer presence. Overall, the proposed CAD method incorporates the advances of image processing, deep learning and image-to-image translation for a biomedical application.

Unsupervised learning method via triple reconstruction for the classification of ultrasound breast lesions

Automatic classification of ultrasound breast tumors has been pragmatically alleviating several defects raised in manual diagnosis and utilized as a second opinion to help diagnostic procedures. The extensive works in literature have excessively tackled this task in a supervised learning frame, but the need for large amounts of annotated data followed by such methods represents one of the fatal drawbacks. To address this, this work proposes an unsupervised learning method based on encoder-decoder scheme that allows to avoid the demand of labeling information in the entire discrimination step.A customized convolutional autoencoder is devised as a backbone architecture. The first step of the scenario is to pre-train the network in a generic way. Then, our strategy is to arbitrarily change the target mapping of the object in the reconstruction loss in order to encapsulate different details in the embedding space. Also, the sequential form of autoencoder is introduced to perform gradual reconstruction, and the mutual information derived from three different representations was generated. The discrimination was performed via subsequent clustering, and thus the aim of this study is to suggest the potentiality of unsupervised learning framework in this field.To verify the effectiveness, experiments were conducted on two publicly available datasets, BUSI and UDIAT. The comprehensive results demonstrate that the mutual information is capable of remarkably enhancing discriminant capability and outperforms the actual supervised learning-based state-of-the-art methods in terms of discrimination. Further, our method has advantages of not requiring any region of interest extraction, localization, or annotated information during training.

A gated convolutional neural network for classification of breast lesions in ultrasound images

A gated convolutional neural network for classification of breast lesions in ultrasound images