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Artificial Intelligence (AI) has revolutionized many fields, including medical imaging. This revolution has enabled the digitization of medical images, the development of algorithms allowing the use of data captured in natural language, and deep learning, enabling the development of algorithms for automatic processing of medical images from massive medical data. In Burkina Faso, early and accurate detection of breast cancer is a significant challenge due to limited resources and lack of specialized expertise. In this article, we examine the effectiveness of different artificial intelligence algorithms for breast cancer detection from pathological image.

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Breast cancer detection at an early stage is crucial for improving patient survival rates. This work introduces an innovative thermal imaging prototype that incorporates compression techniques inspired by mammography equipment. The prototype offers a radiation-free and precise cancer diagnosis. By integrating compression and illumination methods, thermal picture quality has increased, and the accuracy of classification has improved. Essential components of the suggested thermography device include an equipment body, plates, motors, pressure sensors, light sources, and a thermal camera. We created a 3D model of the gadget using the SolidWorks software 2020 package. Furthermore, the classification research employed both cancer and normal images from the experimental results to validate the efficacy of the suggested system. We employed preprocessing and segmentation methods on the obtained dataset. We successfully categorized the thermal pictures using various classifiers and examined their performance. The logistic regression model showed excellent performance, achieving an accuracy of 0.976, F1 score of 0.977, precision of 1.000, and recall of 0.995. This indicates a high level of accuracy in correctly classifying thermal abnormalities associated with breast cancer. The proposed prototype serves as a highly effective tool for conducting initial investigations into breast cancer detection, offering potential advancements in early-stage diagnosis, and improving patient survival rates.

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OBJECTIVES: The randomized TOmosynthesis plus SYnthesized MAmmography (TOSYMA) screening trial has shown that digital breast tomosynthesis plus synthesized mammography (DBT + SM) is superior to digital mammography (DM) in invasive breast cancer detection varying with breast density. On the other hand, the overall average glandular dose (AGD) of DBT is higher than that of DM. Comparing the DBT + SM and DM trial arm, we analyzed here the mean AGD and their determinants per breast density category and related them to the respective invasive cancer detection rates (iCDR). METHODS: TOSYMA screened 99,689 women aged 50 to 69 years. Compression force, resulting breast thickness, the calculated AGD obtained from each mammography device, and previously published iCDR were used for comparisons across breast density categories in the two trial arms. RESULTS: There were 196,622 exposures of 49,227 women (DBT + SM) and 197,037 exposures of 49,132 women (DM) available for analyses. Mean breast thicknesses declined from breast density category A (fatty) to D (extremely dense) in both trial arms. However, while the mean AGD in the DBT + SM arm declined concomitantly from category A (2.41 mGy) to D (1.89 mGy), it remained almost unchanged in the DM arm (1.46 and 1.51 mGy, respectively). In relative terms, the AGD elevation in the DBT + SM arm (64.4% (A), by 44.5% (B), 27.8% (C), and 26.0% (D)) was lowest in dense breasts where, however, the highest iCDR were observed. CONCLUSION: Women with dense breasts may specifically benefit from DBT + SM screening as high cancer detection is achieved with only moderate AGD elevations. CLINICAL RELEVANCE STATEMENT: TOSYMA suggests a favorable constellation for screening with digital breast tomosynthesis plus synthesized mammography (DBT + SM) in dense breasts when weighing average glandular dose elevation against raised invasive breast cancer detection rates. There is potential for density-, i.e., risk-adapted population-wide breast cancer screening with DBT + SM. KEY POINTS: Breast thickness declines with visually increasing density in digital mammography (DM) and digital breast tomosynthesis (DBT). Average glandular doses of DBT decrease with increasing density; digital mammography shows lower and more constant values. With the smallest average glandular dose difference in dense breasts, DBT plus SM had the highest difference in invasive breast cancer detection rates.

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Invasive ductal carcinoma (IDC) in breast specimens has been detected in the quadrant breast area: (I) upper outer, (II) upper inner, (III) lower inner, and (IV) lower outer areas by electrical impedance tomography implemented with Gaussian relaxation-time distribution (EIT-GRTD). The EIT-GRTD consists of two steps which are (1) the optimum frequencyfoptselection and (2) the time constant enhancement of breast imaging reconstruction.foptis characterized by a peak in the majority measurement pair of the relaxation-time distribution functionγ,which indicates the presence of IDC.γrepresents the inverse of conductivity and indicates the response of breast tissues to electrical currents across varying frequencies based on the Voigt circuit model. The EIT-GRTD is quantitatively evaluated by multi-physics simulations using a hemisphere container of mimic breast, consisting of IDC and adipose tissues as normal breast tissue under one condition with known IDC in quadrant breast area II. The simulation results show that EIT-GRTD is able to detect the IDC in four layers atfopt= 30, 170 Hz. EIT-GRTD is applied in the real breast by employed six mastectomy specimens from IDC patients. The placement of the mastectomy specimens in a hemisphere container is an important factor in the success of quadrant breast area reconstruction. In order to perform the evaluation, EIT-GRTD reconstruction images are compared to the CT scan images. The experimental results demonstrate that EIS-GRTD exhibits proficiency in the detection of the IDC in quadrant breast areas while compared qualitatively to CT scan images.

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Reports of mammary Paget's disease (MPD) as a manifestation of breast cancer recurrence are rare. MPD presents a particular challenge when emerging more than two decades after a breast cancer treated with evidence-based therapy. There is a broad spectrum of non-malignant causes for dermatitis of the nipple during the initial presentation that may delay cancer work-up. This case highlights the MPD work-up and management in the context of a personal history of breast cancer. This unique clinical presentation emphasizes the importance of vigilant cancer surveillance for timely intervention, especially for a presumed cured cancer.

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In breast cancer (BC) pathogenesis models, normal cells acquire somatic mutations and there is a stepwise progression from high-risk lesions and ductal carcinoma in situ to invasive cancer. The precancer biology of mammary tissue warrants better characterization to understand how different BC subtypes emerge. Primary methods for BC prevention or risk reduction include lifestyle changes, surgery, and chemoprevention. Surgical intervention for BC prevention involves risk-reducing prophylactic mastectomy, typically performed either synchronously with the treatment of a primary tumor or as a bilateral procedure in high-risk women. Chemoprevention with endocrine therapy carries adherence-limiting toxicity.

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Cancer antigen 15-3 (CA 15-3) is a crucial marker used in the diagnosis and monitoring of breast cancer (BC). The demand for early and precise cancer detection has grown, making the creation of biosensors that are highly sensitive and specific essential. This review paper provides a thorough examination of the progress made in optical and electrochemical biosensors for detecting the cancer biomarker CA 15-3. We focus on explaining their fundamental principles, sensitivity, specificity, and potential for point-of-care applications. The performance attributes of these biosensors are assessed by considering their limits of detection, reaction times, and operational stability, while also making comparisons to conventional methods of CA 15-3 detection. In addition, we explore the incorporation of nanomaterials and innovative transducer components to improve the performance of biosensors. This paper conducts a thorough examination of recent studies to identify the existing obstacles. It also suggests potential areas for future research in this fast progressing field.The paper provides insights into their advancement and utilization to enhance patient outcomes. Both categories of biosensors provide significant promise for the detection of CA 15-3 and offer distinct advantages compared to conventional analytical approaches.

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BACKGROUND: Aberrant DNA methylation has been identified as biomarkers for breast cancer detection. Coiled-coil domain containing 12 gene (CCDC12) implicated in tumorigenesis. This study aims to investigate the potential of blood-based CCDC12 methylation for breast cancer detection. METHODS: DNA methylation level of CpG sites (Cytosine-phosphate Guanine dinucleotides) in CCDC12 gene was measured by mass spectrometry in 255 breast cancer patients, 155 patients with benign breast nodules and 302 healthy controls. The association between CCDC12 methylation and breast cancer risk was evaluated by logistic regression and receiver operating characteristic curve analysis. RESULTS: A total of eleven CpG sites were analyzed. The CCDC12 methylation levels were higher in breast cancer patients. Compared to the lowest tertile of methylation level in CpG_6,7, CpG_10 and CpG_11, the highest quartile was associated with 82, 91 and 95% increased breast cancer risk, respectively. The CCDC12 methylation levels were associated with estrogen receptor (ER) and human epidermal growth factor 2 (HER2) status. In ER-negative and HER2-positive (ER-/HER2+) breast cancer subtype, the combination of four sites CpG_2, CpG_5, CpG_6,7 and CpG_11 methylation levels could distinguish ER-/HER2+ breast cancer from the controls (AUC = 0.727). CONCLUSION: The hypermethylation levels of CCDC12 in peripheral blood could be used for breast cancer detection.

Breast cancer detection could be facilitated by novel blood-based DNA methylation biomarkers.The methylation levels of CpG sites in CCDC12 were higher in breast cancer than those in controls.The combination of four sites CpG_2, CpG_5, CpG_6,7 and CpG_11 methylation levels could distinguish ER-/HER2+ breast cancer subtype from the controls.

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One of the most dangerous conditions in clinical practice is breast cancer because it affects the entire life of women in recent days. Nevertheless, the existing techniques for diagnosing breast cancer are complicated, expensive, and inaccurate. Many trans-disciplinary and computerized systems are recently created to prevent human errors in both quantification and diagnosis. Ultrasonography is a crucial imaging technique for cancer detection. Therefore, it is essential to develop a system that enables the healthcare sector to rapidly and effectively detect breast cancer. Due to its benefits in predicting crucial feature identification from complicated breast cancer datasets, machine learning is widely employed in the categorization of breast cancer patterns. The performance of machine learning models is limited by the absence of a successful feature enhancement strategy. There are a few issues that need to be handled with the traditional breast cancer detection method. Thus, a novel breast cancer detection model is designed based on machine learning approaches and employing ultrasonic images. At first, ultrasound images utilized for the analysis is acquired from the benchmark resources and offered as the input to preprocessing phase. The images are preprocessed by utilizing a filtering and contrast enhancement approach and attained the preprocessed image. Then, the preprocessed images are subjected to the segmentation phase. In this phase, segmentation is performed by employing Fuzzy C-Means, active counter, and watershed algorithm and also attained the segmented images. Later, the segmented images are provided to the pixel selection phase. Here, the pixels are selected by the developed hybrid model Conglomerated Aphid with Galactic Swarm Optimization (CAGSO) to attain the final segmented pixels. Then, the selected segmented pixel is fed in to feature extraction phase for attaining the shape features and the textual features. Further, the acquired features are offered to the optimal weighted feature selection phase, and also their weights are tuned tune by the developed CAGSO. Finally, the optimal weighted features are offered to the breast cancer detection phase. Finally, the developed breast cancer detection model secured an enhanced performance rate than the classical approaches throughout the experimental analysis.

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The most fatal disease affecting women worldwide now is breast cancer. Early detection of breast cancer enhances the likelihood of a full recovery and lowers mortality. Based on medical imaging, researchers from all around the world are developing breast cancer screening technologies. Due to their rapid progress, deep learning algorithms have caught the interest of many in the field of medical imaging. This research proposes a novel method in mammogram image feature extraction with classification and optimization using machine learning in breast cancer detection. The input image has been processed for noise removal, smoothening, and normalization. The input image features were extracted using probabilistic principal component analysis for detecting the presence of tumors in mammogram images. The extracted tumor region is classified using the Naïve Bayes classifier and transfer integrated convolution neural networks. The classified output has been optimized using firefly binary grey optimization and metaheuristic moth flame lion optimization. The experimental analysis has been carried out in terms of different parameters based on datasets. The proposed framework used an ensemble model for breast cancer that made use of the proposed Bayes + FBGO and TCNN + MMFLO classifier and optimizer for diverse mammography image datasets. The INbreast dataset was evaluated using the proposed Bayes + FBGO and TCNN + MMFLO classifiers, which achieved 95% and 98% accuracy, respectively.

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Cancer-related deadly diseases affect both developed and underdeveloped nations worldwide. Effective network learning is crucial to more reliably identify and categorize breast carcinoma in vast and unbalanced image datasets. The absence of early cancer symptoms makes the early identification process challenging. Therefore, from the perspectives of diagnosis, prevention, and therapy, cancer continues to be among the healthcare concerns that numerous researchers work to advance. It is highly essential to design an innovative breast cancer detection model by considering the complications presented in the classical techniques. Initially, breast cancer images are gathered from online sources and it is further subjected to the segmentation region. Here, it is segmented using Adaptive Trans-Dense-Unet (A-TDUNet), and their parameters are tuned using the developed Modified Sheep Flock Optimization Algorithm (MSFOA). The segmented images are further subjected to the breast cancer detection stage and effective breast cancer detection is performed by Multiscale Dilated Densenet with Attention Mechanism (MDD-AM). Throughout the result validation, the Net Present Value (NPV) and accuracy rate of the designed approach are 96.719% and 93.494%. Hence, the implemented breast cancer detection model secured a better efficacy rate than the baseline detection methods in diverse experimental conditions.

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Breast cancer is the second leading cause of death for women worldwide, and detecting cancer at an early stage increases the survival rate by 97%. In this study, a novel textile-based ultrawideband (UWB) microstrip patch antenna was designed and modeled to work in the 2-11.6 GHz frequency range and a simulation was used to test its performance in early breast cancer detection. The antenna was designed with an overall size of 31*31 mm 2 using a denim substrate and 100% metal polyamide-based fabric with copper, silver, and nickel to provide comfort for the wearer. The designed antenna was tested in four numerical breast models. The models ranged from simple tumor-free to complex models with small tumors. The size, structure, and position of the tumor were modified to test the suggested ability of the antenna to detect cancers with different shapes, sizes, and positions. The specific absorption rate (SAR), return loss (S11), and voltage standing wave ratio (VSWR) were calculated for each model to measure the antenna performance. The simulation results showed that SAR values were between 1.6 and 2 W/g (10 g SAR) and were within the allowed range for medical applications. Additionally, the VSWR remained in an acceptable range from 1.15 to 2. Depending on the size and location of the tumor, the antenna return losses of the four models ranged from - 36 to - 18.5 dB. The effect of bending was tested to determine the flexibility. The antenna proved to be highly effective and capable of detecting small tumors with diameters of up to 2 mm.

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This study proposes using magnetically induced currents in medical infrared imaging to increase the temperature contrast due to the electrical conductivity differences between tumors and healthy tissues. There are two objectives: (1) to investigate the feasibility of this active method for surface and deep tumors using numerical simulations, and (2) to demonstrate the use of this method through different experiments conducted with phantoms that mimic breast tissues. Tumorous breasts were numerically modeled and simulated in active and passive modes. At 750 kHz, the applied current was limited for breast tissue-tumor conductivities (0.3 S/m and 0.75 S/m) according to the local specific absorption rate limit of 10 W/kg. Gelatin-based and mashed potato phantoms were produced to mimic tumorous breast tissues. In the simulation studies, the induced current changed the temperature contrast on the imaging surface, and the tumor detection sensitivity increased by 4 mm. An 11-turn 70-mm-long solenoid coil was constructed, 20 A current was applied for deep tumors, and a difference of up to 0.4  ∘ C was observed in the tumor location compared with the temperature in the absence of the tumor. Similarly, a 23-turn multi-layer coil was constructed, and a temperature difference of 0.4  ∘ C was observed. The temperature contrast on the body surface changed, and the tumor detection depth increased with the induced currents in breast IR imaging. The proposed active thermal imaging method was validated using numerical simulations and in vitro experiments.

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Breast cancer is a major cause of female deaths, especially in underdeveloped countries. It can be treated if diagnosed early and chances of survival are high if treated appropriately and timely. For timely and accurate automated diagnosis, machine learning approaches tend to show better results than traditional methods, however, accuracy lacks the desired level. This study proposes the use of an ensemble model to provide accurate detection of breast cancer. The proposed model uses the random forest and support vector classifier along with automatic feature extraction using an optimized convolutional neural network (CNN). Extensive experiments are performed using the original, as well as, CNN-based features to analyze the performance of the deployed models. Experimental results involving the use of the Wisconsin dataset reveal that CNN-based features provide better results than the original features. It is observed that the proposed model achieves an accuracy of 99.99% for breast cancer detection. Performance comparison with existing state-of-the-art models is also carried out showing the superior performance of the proposed model.

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BACKGROUND: Some researchers have questioned whether artificial intelligence (AI) systems maintain their performance when used for women from populations not considered during the development of the system. PURPOSE: To evaluate the impact of transfer learning as a way of improving the generalization of AI systems in the detection of breast cancer. MATERIAL AND METHODS: This retrospective case-control Finnish study involved 191 women diagnosed with breast cancer and 191 matched healthy controls. We selected a state-of-the-art AI system for breast cancer detection trained using a large US dataset. The selected baseline system was evaluated in two experimental settings. First, we examined our private Finnish sample as an independent test set that had not been considered in the development of the system (unseen population). Second, the baseline system was retrained to attempt to improve its performance in the unseen population by means of transfer learning. To analyze performance, we used areas under the receiver operating characteristic curve (AUCs) with DeLong's test. RESULTS: Two versions of the baseline system were considered: ImageOnly and Heatmaps. The ImageOnly and Heatmaps versions yielded mean AUC values of 0.82±0.008 and 0.88±0.003 in the US dataset and 0.56 (95% CI=0.50-0.62) and 0.72 (95% CI=0.67-0.77) when evaluated in the unseen population, respectively. The retrained systems achieved AUC values of 0.61 (95% CI=0.55-0.66) and 0.69 (95% CI=0.64-0.75), respectively. There was no statistical difference between the baseline system and the retrained system. CONCLUSION: Transfer learning with a small study sample did not yield a significant improvement in the generalization of the system.

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Nipple discharge presents as either physiological, which is green, white, or yellow, or is considered pathological, which is typically unilateral, spontaneous, and bloody. Bloody nipple discharge (BND) can be due to underlying malignancy or premalignant lesions. Mammogram (MMG), ultrasound (US), MRI, and ductography are all used to evaluate BND, but different modalities offer greater value in the diagnostic process. Here, we present a case that demonstrates the ability of MRI to detect abnormalities not seen on MMG and US in the setting of BND due to underlying malignancy. The use of MRI earlier in the diagnostic process allows for the use of breast-conserving measures and decreases the possibility of metastasis. This would result in less of a need for more aggressive treatments.

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Replacing lung cancer as the most commonly diagnosed cancer globally, breast cancer (BC) today accounts for 1 in 8 cancer diagnoses and a total of 2.3 million new cases in both sexes combined. An estimated 685,000 women died from BC in 2020, corresponding to 16% or 1 in every 6 cancer deaths in women. BC represents a quarter of a total of cancer cases in females and by far the most commonly diagnosed cancer in women in 2020. However, when detected in the early stages of the disease, treatment methods have proven to be very effective in increasing life expectancy and, in many cases, patients fully recover. Several medical imaging modalities, such as X-rays Mammography (MG), Ultrasound (US), Computer Tomography (CT), Magnetic Resonance Imaging (MRI), and Digital Tomosynthesis (DT) have been explored to support radiologists/physicians in clinical decision-making workflows for the detection and diagnosis of BC. In this work, we propose a novel Faster R-CNN-based framework to automate the detection of BC pathological Lesions in MRI. As a main contribution, we have developed and experimentally (statistically) validated an innovative method improving the "breast MRI preprocessing phase" to select the patient's slices (images) and associated bounding boxes representing pathological lesions. In this way, it is possible to create a more robust training (benchmarking) dataset to feed Deep Learning (DL) models, reducing the computation time and the dimension of the dataset, and more importantly, to identify with high accuracy the specific regions (bounding boxes) for each of the patient's images, in which a possible pathological lesion (tumor) has been identified. As a result, in an experimental setting using a fully annotated dataset (released to the public domain) comprising a total of 922 MRI-based BC patient cases, we have achieved, as the most accurate trained model, an accuracy rate of 97.83%, and subsequently, applying a ten-fold cross-validation method, a mean accuracy on the trained models of 94.46% and an associated standard deviation of 2.43%.

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This review evaluates the methods used for image quality analysis and tumour detection in experimental breast microwave sensing (BMS), a developing technology being investigated for breast cancer detection. This article examines the methods used for image quality analysis and the estimated diagnostic performance of BMS for image-based and machine-learning tumour detection approaches. The majority of image analysis performed in BMS has been qualitative and existing quantitative image quality metrics aim to describe image contrast-other aspects of image quality have not been addressed. Image-based diagnostic sensitivities between 63 and 100% have been achieved in eleven trials, but only four articles have estimated the specificity of BMS. The estimates range from 20 to 65%, and do not demonstrate the clinical utility of the modality. Despite over two decades of research in BMS, significant challenges remain that limit the development of this modality as a clinical tool. The BMS community should utilize consistent image quality metric definitions and include image resolution, noise, and artifacts in their analyses. Future work should include more robust metrics, estimates of the diagnostic specificity of the modality, and machine-learning applications should be used with more diverse datasets and with robust methodologies to further enhance BMS as a viable clinical technique.

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This paper presents a novel framework for breast cancer detection using mammogram images. The proposed solution aims to output an explainable classification from a mammogram image. The classification approach uses a Case-Based Reasoning system (CBR). CBR accuracy strongly depends on the quality of the extracted features. To achieve relevant classification, we propose a pipeline that includes image enhancement and data augmentation to improve the quality of extracted features and provide a final diagnosis. An efficient segmentation method based on a U-Net architecture is used to extract Regions of interest (RoI) from mammograms. The purpose is to combine deep learning (DL) with CBR to improve classification accuracy. DL provides accurate mammogram segmentation, while CBR gives an explainable and accurate classification. The proposed approach was tested on the CBIS-DDSM dataset and achieved high performance with an accuracy (Acc) of 86.71 % and a recall of 91.34 %, outperforming some well-known machine learning (ML) and DL approaches.

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In this paper, a deep learning technique for tumor detection in a microwave tomography framework is proposed. Providing an easy and effective imaging technique for breast cancer detection is one of the main focuses for biomedical researchers. Recently, microwave tomography gained a great attention due to its ability to reconstruct the electric properties maps of the inner breast tissues, exploiting nonionizing radiations. A major drawback of tomographic approaches is related to the inversion algorithms, since the problem at hand is nonlinear and ill-posed. In recent decades, numerous studies focused on image reconstruction techniques, in same cases exploiting deep learning. In this study, deep learning is exploited to provide information about the presence of tumors based on tomographic measures. The proposed approach has been tested with a simulated database showing interesting performances, in particular for scenarios where the tumor mass is particularly small. In these cases, conventional reconstruction techniques fail in identifying the presence of suspicious tissues, while our approach correctly identifies these profiles as potentially pathological. Therefore, the proposed method can be exploited for early diagnosis purposes, where the mass to be detected can be particularly small.