Cancer In Mammograms Research Articles

Detection of multiple abnormalities of breast cancer in mammograms using a deep dilated fully convolutional neural network

Breast cancer is one of the serious health issues which are quite frequent among women and the mortality rate due to it can be reduced if detected and diagnosed at the early stage. In this paper, a new generalized deep learning architecture, deep dilated fully convolutional neural network (DDFCNN), is designed to detect multiple abnormalities like mass, microcalcification, and architectural distortion in mammograms. The proposed DDFCNN architecture consists of a feature and a dilation module. In feature module, convolutional kernels of various sizes are used to capture information at different scales. Since there is no loss of resolution in the output of the dilation module, it renders more details which helps in localizing the anomaly properly. Several experiments are conducted on a widely used databases — DDSM and mini-MIAS where an accuracy of 95.33%, 91.42%, and 92.67% is measured for detection of mass, microcalcification, and architectural distortion, respectively with a FPs/I of 0.43, 0.50, and 0.46 for DDSM and similar assessment for mini-MIAS database are 96.56%, 89.63%, and 95.07% with a FPs/I of 0.29, 0.51, and 0.31. Moreover, the overall performance in detection of all the abnormalities in combination is 93.14% with 0.46 as FPs/I for DDSM whereas for mini-MIAS is 93.75% with 0.37 as FPs/I. The performance is further compared with the state-of-the-other methods where the proposed approach takes an edge.

The message matters: changes to binary Computer Aided Detection recommendations affect cancer detection in low prevalence search

Computer Aided Detection (CAD) has been used to help readers find cancers in mammograms. Although these automated systems have been shown to help cancer detection when accurate, the presence of CAD also leads to an over-reliance effect where miss errors and false alarms increase when the CAD system fails. Previous research investigated CAD systems which overlayed salient exogenous cues onto the image to highlight suspicious areas. These salient cues capture attention which may exacerbate the over-reliance effect. Furthermore, overlaying CAD cues directly on the mammogram occludes sections of breast tissue which may disrupt global statistics useful for cancer detection. In this study we investigated whether an over-reliance effect occurred with a binary CAD system, which instead of overlaying a CAD cue onto the mammogram, reported a message alongside the mammogram indicating the possible presence of a cancer. We manipulated the certainty of the message and whether it was presented only to indicate the presence of a cancer, or whether a message was displayed on every mammogram to state whether a cancer was present or absent. The results showed that although an over-reliance effect still occurred with binary CAD systems miss errors were reduced when the CAD message was more definitive and only presented to alert readers of a possible cancer.

New technologies in medicine and healthcare: benefits and drawbacks

Advancements in technology have revolutionized the healthcare industry, enabling us to detect, diagnose,and treat medical conditions with greater accuracy and speed than ever before. From electronic health recordsto telemedicine, these technologies are improving patient outcomes, enhancing communication between healthcareprofessionals, and reducing healthcare costs.One of the most exciting technologies in medicine is Artificial Intelligence (AI). AI algorithms can analyzevast amounts of medical data and provide insights into diseases that would be impossible for humansto identify [1]. For example, AI has been used to identify early signs of cardiovascular diseases and to detectbreast cancer in mammograms with greater accuracy than human radiologists [2]. However, some expertshave expressed concern that AI could replace human doctors, leading to a lack of empathy and a loss of thepersonal touch in healthcare.Telemedicine is another technology that is gaining popularity, particularly in rural areas where access tohealthcare can be limited. Telemedicine allows doctors to consult with patients remotely, using video conferencingand other technologies to diagnose and treat medical conditions [3]. This can save patients time andmoney, as well as reduce the burden on healthcare providers. However, telemedicine is not suitable for allmedical conditions, and there are concerns about privacy and security when it comes to transmitting sensitivemedical information over the internet.Electronic Health Records (EHRs) are another technology that is transforming healthcare. EHRs enablehealthcare providers to access a patient’s medical history, medications, and test results quickly and easily.This can improve patient outcomes by reducing errors and providing doctors with a complete picture of apatient’s health. However, there are concerns about data privacy and security when it comes to storing sensitivemedical information electronically. Some solutions have arrived, as blockchain technologies [4].In conclusion, technology has the potential to transform healthcare and improve patient outcomes. Thesetechnologies offer exciting possibilities for the future of medicine. However, it is important to consider theethical, privacy, and security implications of these technologies and to ensure that they are used in a waythat benefits patients without compromising their rights or safety.

Evaluation of an AI Model to Assess Future Breast Cancer Risk.

Background Accurate breast cancer risk assessment after a negative screening result could enable better strategies for early detection. Purpose To evaluate a deep learning algorithm for risk assessment based on digital mammograms. Materials and Methods A retrospective observational matched case-control study was designed using the OPTIMAM Mammography Image Database from the National Health Service Breast Screening Programme in the United Kingdom from February 2010 to September 2019. Patients with breast cancer (cases) were diagnosed following a mammographic screening or between two triannual screening rounds. Controls were matched based on mammography device, screening site, and age. The artificial intelligence (AI) model only used mammograms at screening before diagnosis. The primary objective was to assess model performance, with a secondary objective to assess heterogeneity and calibration slope. The area under the receiver operating characteristic curve (AUC) was estimated for 3-year risk. Heterogeneity according to cancer subtype was assessed using a likelihood ratio interaction test. Statistical significance was set at P < .05. Results Analysis included patients with screen-detected (median age, 60 years [IQR, 55-65 years]; 2044 female, including 1528 with invasive cancer and 503 with ductal carcinoma in situ [DCIS]) or interval (median age, 59 years [IQR, 53-65 years]; 696 female, including 636 with invasive cancer and 54 with DCIS) breast cancer and 1:1 matched controls, each with a complete set of mammograms at the screening preceding diagnosis. The AI model had an overall AUC of 0.68 (95% CI: 0.66, 0.70), with no evidence of a significant difference between interval and screen-detected (AUC, 0.69 vs 0.67; P = .085) cancer. The calibration slope was 1.13 (95% CI: 1.01, 1.26). There was similar performance for the detection of invasive cancer versus DCIS (AUC, 0.68 vs 0.66; P = .057). The model had higher performance for advanced cancer risk (AUC, 0.72 ≥stage II vs 0.66 <stage II; P = .037). The AUC for detecting breast cancer in mammograms at diagnosis was 0.89 (95% CI: 0.88, 0.91). Conclusion The AI model was found to be a strong predictor of breast cancer risk for 3-6 years following a negative mammographic screening. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Mann and Sechopoulos in this issue.

A divide and conquer approach to maximise deep learning mammography classification accuracies.

Breast cancer claims 11,400 lives on average every year in the UK, making it one of the deadliest diseases. Mammography is the gold standard for detecting early signs of breast cancer, which can help cure the disease during its early stages. However, incorrect mammography diagnoses are common and may harm patients through unnecessary treatments and operations (or a lack of treatment). Therefore, systems that can learn to detect breast cancer on their own could help reduce the number of incorrect interpretations and missed cases. Various deep learning techniques, which can be used to implement a system that learns how to detect instances of breast cancer in mammograms, are explored throughout this paper. Convolution Neural Networks (CNNs) are used as part of a pipeline based on deep learning techniques. A divide and conquer approach is followed to analyse the effects on performance and efficiency when utilising diverse deep learning techniques such as varying network architectures (VGG19, ResNet50, InceptionV3, DenseNet121, MobileNetV2), class weights, input sizes, image ratios, pre-processing techniques, transfer learning, dropout rates, and types of mammogram projections. This approach serves as a starting point for model development of mammography classification tasks. Practitioners can benefit from this work by using the divide and conquer results to select the most suitable deep learning techniques for their case out-of-the-box, thus reducing the need for extensive exploratory experimentation. Multiple techniques are found to provide accuracy gains relative to a general baseline (VGG19 model using uncropped 512 × 512 pixels input images with a dropout rate of 0.2 and a learning rate of 1 × 10-3) on the Curated Breast Imaging Subset of DDSM (CBIS-DDSM) dataset. These techniques involve transfer learning pre-trained ImagetNet weights to a MobileNetV2 architecture, with pre-trained weights from a binarised version of the mini Mammography Image Analysis Society (mini-MIAS) dataset applied to the fully connected layers of the model, coupled with using weights to alleviate class imbalance, and splitting CBIS-DDSM samples between images of masses and calcifications. Using these techniques, a 5.6% gain in accuracy over the baseline model was accomplished. Other deep learning techniques from the divide and conquer approach, such as larger image sizes, do not yield increased accuracies without the use of image pre-processing techniques such as Gaussian filtering, histogram equalisation and input cropping.

Framing the fallibility of Computer-Aided Detection aids cancer detection

Computer-Aided Detection (CAD) has been proposed to help operators search for cancers in mammograms. Previous studies have found that although accurate CAD leads to an improvement in cancer detection, inaccurate CAD leads to an increase in both missed cancers and false alarms. This is known as the over-reliance effect. We investigated whether providing framing statements of CAD fallibility could keep the benefits of CAD while reducing over-reliance. In Experiment 1, participants were told about the benefits or costs of CAD, prior to the experiment. Experiment 2 was similar, except that participants were given a stronger warning and instruction set in relation to the costs of CAD. The results showed that although there was no effect of framing in Experiment 1, a stronger message in Experiment 2 led to a reduction in the over-reliance effect. A similar result was found in Experiment 3 where the target had a lower prevalence. The results show that although the presence of CAD can result in over-reliance on the technology, these effects can be mitigated by framing and instruction sets in relation to CAD fallibility.

Convolutional Feature Descriptor Selection for Mammogram Classification.

Breast cancer was the most commonly diagnosed cancer among women worldwide in 2020. Recently, several deep learning-based classification approaches have been proposed to screen breast cancer in mammograms. However, most of these approaches require additional detection or segmentation annotations. Meanwhile, some other image-level label-based methods often pay insufficient attention to lesion areas, which are critical for diagnosis. This study designs a novel deep-learning method for automatically diagnosing breast cancer in mammography, which focuses on the local lesion areas and only utilizes image-level classification labels. In this study, we propose to select discriminative feature descriptors from feature maps instead of identifying lesion areas using precise annotations. And we design a novel adaptive convolutional feature descriptor selection (AFDS) structure based on the distribution of the deep activation map. Specifically, we adopt the triangle threshold strategy to calculate a specific threshold for guiding the activation map to determine which feature descriptors (local areas) are discriminative. Ablation experiments and visualization analysis indicate that the AFDS structure makes the model easier to learn the difference between malignant and benign/normal lesions. Furthermore, since the AFDS structure can be regarded as a highly efficient pooling structure, it can be easily plugged into most existing convolutional neural networks with negligible effort and time consumption. Experimental results on two publicly available INbreast and CBIS-DDSM datasets indicate that the proposed method performs satisfactorily compared with state-of-the-art methods.

Development of an Artificial Intelligence-Based Breast Cancer Detection Model by Combining Mammograms and Medical Health Records.

Artificial intelligence (AI)-based computational models that analyze breast cancer have been developed for decades. The present study was implemented to investigate the accuracy and efficiency of combined mammography images and clinical records for breast cancer detection using machine learning and deep learning classifiers. This study was verified using 731 images from 357 women who underwent at least one mammogram and had clinical records for at least six months before mammography. The model was trained on mammograms and clinical variables to discriminate benign and malignant lesions. Multiple pre-trained deep CNN models to detect cancer in mammograms, including X-ception, VGG16, ResNet-v2, ResNet50, and CNN3 were employed. Machine learning models were constructed using k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), Artificial Neural Network (ANN), and gradient boosting machine (GBM) in the clinical dataset. The detection performance obtained an accuracy of 84.5% with a specificity of 78.1% at a sensitivity of 89.7% and an AUC of 0.88. When trained on mammography image data alone, the result achieved a slightly lower score than the combined model (accuracy, 72.5% vs. 84.5%, respectively). A breast cancer-detection model combining machine learning and deep learning models was performed in this study with a satisfactory result, and this model has potential clinical applications.

Using global feedback to induce learning of gist of abnormality in mammograms

Extraction of global structural regularities provides general ‘gist’ of our everyday visual environment as it does the gist of abnormality for medical experts reviewing medical images. We investigated whether naïve observers could learn this gist of medical abnormality. Fifteen participants completed nine adaptive training sessions viewing four categories of unilateral mammograms: normal, obvious-abnormal, subtle-abnormal, and global signals of abnormality (mammograms with no visible lesions but from breasts contralateral to or years prior to the development of cancer) and receiving only categorical feedback. Performance was tested pre-training, post-training, and after a week’s retention on 200 mammograms viewed for 500 ms without feedback. Performance measured as d’ was modulated by mammogram category, with the highest performance for mammograms with visible lesions. Post-training, twelve observed showed increased d’ for all mammogram categories but a subset of nine, labelled learners also showed a positive correlation of d’ across training. Critically, learners learned to detect abnormality in mammograms with only the global signals, but improvements were poorly retained. A state-of-the-art breast cancer classifier detected mammograms with lesions but struggled to detect cancer in mammograms with the global signal of abnormality. The gist of abnormality can be learned through perceptual/incidental learning in mammograms both with and without visible lesions, subject to individual differences. Poor retention suggests perceptual tuning to gist needs maintenance, converging with findings that radiologists’ gist performance correlates with the number of cases reviewed per year, not years of experience. The human visual system can tune itself to complex global signals not easily captured by current deep neural networks.

Attention-Based Active Learning Framework for Segmentation of Breast Cancer in Mammograms

Breast cancer is one of most serious malignant tumors that affect women’s health. To carry out the early screening of breast cancer, mammography provides breast cancer images for doctors’ efficient diagnosis. However, breast cancer lumps can vary in size and shape, bringing difficulties for the accurate recognition of both humans and machines. Moreover, the annotation of such images requires expert medical knowledge, which increases the cost of collecting datasets to boost the performance of deep learning methods. To alleviate these problems, we propose an attention-based active learning framework for breast cancer segmentation in mammograms; the framework consists of a basic breast cancer segmentation model, an attention-based sampling scheme and an active learning strategy for labelling. The basic segmentation model performs multi-scale feature fusion and enhancement on the basis of UNet, thus improving the distinguishing representation capability of the extracted features for further segmentation. Afterwards, the proposed attention-based sampling scheme assigns different weights for unlabeled breast cancer images by evaluating their uncertainty with the basic segmentation model. Finally, the active learning strategy selects unlabeled images with the highest weights for manual labeling, thus boosting the performance of the basic segmentation model via retraining with new labeled samples. Testing on four datasets, experimental results show that the proposed framework could greatly improve segmentation accuracy by about 15% compared with an existing method, while largely decreasing the cost of data annotation.

Early detection of breast cancer in mammograms using the lightweight modification of efficientNet B3

Breast cancer is one of the leading causes of death in women worldwide and early detection is critical to improving survival rates. In this study, we present a modified deep learning method for automatic feature detection for breast mass classification on mammograms. We propose to use EfficientNet, a Convolutional Neural Network (CNN) architecture that requires minimal parameters. The main advantage of EfficientNet is the small number of parameters, which allows efficient and accurate classification of mammogram images. Our experiments show that EfficientNet, with an overall accuracy of 86.5%, has the potential to be the fundamental for a fully automated and effective breast cancer detection system in the future. Our results demonstrate the potential of EfficientNet to improve the accuracy and efficiency of breast cancer detection compared to other approaches.

Case-Based Approach to Detect Cancer in Women with Curative Intent at Beginning

Background: Breast malignant growth is among the most widely recognized ladies’ diseases and a significant reason for disease-delivered ladies' deaths all over the planet. Mammograms have a high pace of missed growths, or "misleading negatives." Over 10% of threatening cancers in mammograms cannot be identified in ladies more than 50 years. Objective: To Increase the detection rate of mammography for detection of early breast cancer. Methods: The research work was conducted during 2022-2023 at Radiation Oncology, Allama Iqbal Medical College/Jinnah Hospital, Lahore with data of thousands of cases. Case-based reasoning (CBR) is a strategy to apply problem solution data of currently tackled issues by the arrangement of coming issues. Results: CBR characterizes benign cases with precision and recall of equivalent to 0.87 and 0.7 and for malignant cases are 0.9 and precision of 0.75 respectively. Our objective is to improve the detection of cancer with Principal Component analysis of characteristics, precision enhanced by 20% and recall by 11% for malignant cases and by 15% and 28.5% for benign cases respectively. The outcomes acquired by CBR are compared with multiple Knowledge-based algorithms. Conclusion: The CBR-based approach delivered improved results when contrasted with the wide range of various strategies regarding precision, recall, misleading negative rate, genuine positive rate and F-measure for dangerous cases.

Biasing Influence of 'Mental Shortcuts' on Diagnostic Decision-Making: Radiologists Can Overlook Breast Cancer in Mammograms When Prior Diagnostic Information Is Available.

When making decisions under uncertainty, people in all walks of life, including highly trained medical professionals, tend to resort to using ‘mental shortcuts’, or heuristics. Anchoring-and-adjustment (AAA) is a well-known heuristic in which subjects reach a judgment by starting from an initial internal judgment (‘anchored position’) based on available external information (‘anchoring information’) and adjusting it until they are satisfied. We studied the effects of the AAA heuristic during diagnostic decision-making in mammography. We provided practicing radiologists (N = 27 across two studies) a random number that we told them was the estimate of a previous radiologist of the probability that a mammogram they were about to see was positive for breast cancer. We then showed them the actual mammogram. We found that the radiologists’ own estimates of cancer in the mammogram reflected the random information they were provided and ignored the actual evidence in the mammogram. However, when the heuristic information was not provided, the same radiologists detected breast cancer in the same set of mammograms highly accurately, indicating that the effect was solely attributable to the availability of heuristic information. Thus, the effects of the AAA heuristic can sometimes be so strong as to override the actual clinical evidence in diagnostic tasks.

Evaluation of deep learning models for detecting breast cancer using histopathological mammograms Images

Breast cancer detection based on the deep learning approach has gained much interest among other conventional-based CAD systems as the conventional based CAD system's accuracy results seems to be inadequate. The convolution neural network, a deep learning approach, has emerged as the most promising technique for detecting cancer in mammograms. In this paper we delve into some of the CNN classifiers used to detect breast cancer by classifying mammogram images into benign, cancer, or normal class. Our study evaluated the performance of various CNN architectures such as AlexNet, VGG16, and ResNet50 by training some of them from scratch and some using transfer learning with pre-trained weights. The above model classifiers are trained and tested using mammogram images from the mini-DDSM dataset which is publicly available. The medical dataset contains limited samples of data due to low patient volume; this can lead to overfitting issue, so to overcome this limitation data augmentation process is applied. Rotation and zooming techniques are applied to increase the data volume. The validation strategy used here is 90:10 ratio. AlexNet showed an accuracy of 65 percent, whereas VGG16 and ResNet50 showed an accuracy of 65% and 61%, respectively when fine-tuned with pre-trained weights. VGG16 performed significantly worse when trained from scratch, whereas AlexNet outperformed others. VGG16 and ResNet50 performed well when transfer learning was applied.

Breast Cancer Diagnosis in Two-View Mammography Using End-to-End Trained EfficientNet-Based Convolutional Network

Some recent studies have described deep convolutional neural networks to diagnose breast cancer in mammograms with similar or even superior performance to that of human experts. One of the best techniques does two transfer learnings: the first uses a model trained on natural images to create a "patch classifier" that categorizes small subimages; the second uses the patch classifier to scan the whole mammogram and create the "single-view whole-image classifier". We propose to make a third transfer learning to obtain a "two-view classifier" to use the two mammographic views: bilateral craniocaudal and mediolateral oblique. We use EfficientNet as the basis of our model. We "end-to-end" train the entire system using CBIS-DDSM dataset. To ensure statistical robustness, we test our system twice using: (a) 5-fold cross validation; and (b) the original training/test division of the dataset. Our technique reached an AUC of 0.9344 using 5-fold cross validation (accuracy, sensitivity and specificity are 85.13% at the equal error rate point of ROC). Using the original dataset division, our technique achieved an AUC of 0.8483, as far as we know the highest reported AUC for this problem, although the subtle differences in the testing conditions of each work do not allow for an accurate comparison. The inference code and model are available at https://github.com/dpetrini/two-views-classifier

Classification of Breast Cancer in Mammograms with Deep Learning Adding a Fifth Class

Breast cancer is one of the diseases of most profound concern, with the most prevalence worldwide, where early detections and diagnoses play the leading role against this disease achieved through imaging techniques such as mammography. Radiologists tend to have a high false positive rate for mammography diagnoses and an accuracy of around 82%. Currently, deep learning (DL) techniques have shown promising results in the early detection of breast cancer by generating computer-aided diagnosis (CAD) systems implementing convolutional neural networks (CNNs). This work focuses on applying, evaluating, and comparing the architectures: AlexNet, GoogLeNet, Resnet50, and Vgg19 to classify breast lesions after using transfer learning with fine-tuning and training the CNN with regions extracted from the MIAS and INbreast databases. We analyzed 14 classifiers, involving 4 classes as several researches have done it before, corresponding to benign and malignant microcalcifications and masses, and as our main contribution, we also added a 5th class for the normal tissue of the mammary parenchyma increasing the correct detection; in order to evaluate the architectures with a statistical analysis based on the received operational characteristics (ROC), the area under the curve (AUC), F1 Score, accuracy, precision, sensitivity, and specificity. We generate the best results with the CNN GoogLeNet trained with five classes on a balanced database with an AUC of 99.29%, F1 Score of 91.92%, the accuracy of 91.92%, precision of 92.15%, sensitivity of 91.70%, and specificity of 97.66%, concluding that GoogLeNet is optimal as a classifier in a CAD system to deal with breast cancer.

DeepMiner: Discovering Interpretable Representations for Mammogram Classification and Explanation

We propose DeepMiner, a framework to discover interpretable representations in deep neural networks and to build explanations for medical predictions. By probing convolutional neural networks (CNNs) trained to classify cancer in mammograms, we show that many individual units in the final convolutional layer of a CNN respond strongly to diseased tissue concepts specified by the Breast Imaging-Reporting and Data System (BI-RADS) lexicon. After expert annotation of the interpretable units, our proposed method is able to generate explanations for CNN mammogram classification that are consistent with ground truth radiology reports on the Digital Database for Screening Mammography. We show that DeepMiner not only enables better understanding of the nuances of CNN classification decisions but also possibly discovers new visual knowledge relevant to medical diagnosis.

Breast cancer: One-stage automated detection, segmentation, and classification of digital mammograms using UNet model based-semantic segmentation

Breast cancer is one of the most common cancers in women. It is known as asymptomatic cancer that presents no noticeable symptoms in its early stage. Thus, regular mammography screening helps detect breast cancer early before it spreads to nearby normal tissues or to other organs. Hence, an automated system for early detection of breast cancer in mammograms will assist radiologists in their diagnosis. The rapid advance of deep learning models has considerably arisen much interest in their application to medical imaging problems, particularly for breast cancer diagnosis. In this paper, we propose an end-to-end UNet model for the detection, segmentation, and classification of breast masses in one-stage. The proposed model is evaluated in terms of its performance in segmenting and classifying breast masses using the publicly available datasets, DDSM and INbreast. The mass segmentation and classification evaluation results give an intersection over union (IOU) score of 90.50% and an area under the curve of 99.88% for both DDSM and INbreast datasets. The proposed model scores a dice coefficient of 99.20% and 99.56% and a weighted F1-score of 99.19% and 99.65% for the DDSM and INbreast datasets, respectively. The results show that the proposed model outperforms the existing methodologies.

Integrating segmentation information into CNN for breast cancer diagnosis of mammographic masses

Background and ObjectivesSegmentation of mammographic lesions has been proven to be a valuable source of information, as it can assist in both extracting shape-related features and providing accurate localization of the lesion. In this work, a methodology is proposed for integrating mammographic mass segmentation information into a convolutional neural network (CNN), aiming to improve the diagnosis of breast cancer in mammograms. MethodsThe proposed methodology involves modification of each convolutional layer of a CNN, so that information of not only the input image but also the corresponding segmentation map is considered. Furthermore, a new loss function is introduced, which adds an extra term to the standard cross-entropy, aiming to steer the attention of the network to the mass region, penalizing strong feature activations based on their location. The segmentation maps are acquired either from the provided ground-truth or from an automatic segmentation stage. ResultsPerformance evaluation in diagnosis is conducted on two mammographic mass datasets, namely DDSM-400 and CBIS-DDSM, with differences in quality of the corresponding ground-truth segmentation maps. The proposed method achieves diagnosis performance of 0.898 and 0.862 in terms AUC when using ground-truth segmentation maps and a maximum of 0.880 and 0.860 when a U-Net-based automatic segmentation stage is employed, for DDSM-400 and CBIS-DDSM, respectively. ConclusionsThe experimental results demonstrate that integrating segmentation information into a CNN leads to improved performance in breast cancer diagnosis of mammographic masses.

Using Convolutional Neural Network with Cheat Sheet and Data Augmentation to Detect Breast Cancer in Mammograms.

The American Cancer Society expected to diagnose 276,480 new cases of invasive breast cancer in the USA and 48,530 new cases of noninvasive breast cancer among women in 2020. Early detection of breast cancer, followed by appropriate treatment, can reduce the risk of death from this disease. DL through CNN can assist imaging specialists in classifying the mammograms accurately. Accurate classification of mammograms using CNN needs a well-trained CNN by a large number of labeled mammograms. Unfortunately, a large number of labeled mammograms are not always available. In this study, a novel procedure to aid imaging specialists in detecting normal and abnormal mammograms has been proposed. The procedure supplied the designed CNN with a cheat sheet for some classical attributes extracted from the ROI and an extra number of labeled mammograms through data augmentation. The cheat sheet aided the CNN through encoding easy-to-recognize artificial patterns in the mammogram before passing it to the CNN, and the data augmentation supported the CNN with more labeled data points. Fifteen runs of 4 different modified datasets taken from the MIAS dataset were conducted and analyzed. The results showed that the cheat sheet, along with data augmentation, enhanced CNN's accuracy by at least 12.2% and enhanced the precision of the CNN by at least 2.2. The mean accuracy, sensitivity, and specificity obtained using the proposed procedure were 92.1, 91.4, and 96.8, respectively, while the average area under the ROC curve was 94.9.