Technique For Detection Of Breast Cancer Research Articles

Deep learning approaches for breast cancer detection in histopathology images: A review.

Breast cancer is one of the leading causes of death in women worldwide. Histopathology analysis of breast tissue is an essential tool for diagnosing and staging breast cancer. In recent years, there has been a significant increase in research exploring the use of deep-learning approaches for breast cancer detection from histopathology images. To provide an overview of the current state-of-the-art technologies in automated breast cancer detection in histopathology images using deep learning techniques. This review focuses on the use of deep learning algorithms for the detection and classification of breast cancer from histopathology images. We provide an overview of publicly available histopathology image datasets for breast cancer detection. We also highlight the strengths and weaknesses of these architectures and their performance on different histopathology image datasets. Finally, we discuss the challenges associated with using deep learning techniques for breast cancer detection, including the need for large and diverse datasets and the interpretability of deep learning models. Deep learning techniques have shown great promise in accurately detecting and classifying breast cancer from histopathology images. Although the accuracy levels vary depending on the specific data set, image pre-processing techniques, and deep learning architecture used, these results highlight the potential of deep learning algorithms in improving the accuracy and efficiency of breast cancer detection from histopathology images. This review has presented a thorough account of the current state-of-the-art techniques for detecting breast cancer using histopathology images. The integration of machine learning and deep learning algorithms has demonstrated promising results in accurately identifying breast cancer from histopathology images. The insights gathered from this review can act as a valuable reference for researchers in this field who are developing diagnostic strategies using histopathology images. Overall, the objective of this review is to spark interest among scholars in this complex field and acquaint them with cutting-edge technologies in breast cancer detection using histopathology images.

Review on Present-day Breast Cancer Detection Techniques

Breast cancer remains a prevalent health complication among the female population. Early and reliable detection in an individual is necessary for effective treatment. Thus, R&D into techniques for detection of breast cancer continues to the present. Non-invasive techniques include tactile examinations, electromagnetic scanning and checks for chemical markers. Invasive techniques include biopsies that extract tissue and liquid samples. These techniques have limitations and setbacks that are being addressed with supplementary or complementary techniques. Like the pre-existing techniques, these techniques also rely on comparison of data between control samples and afflicted patients to measure their reliability. Therefore, R&D efforts towards detection of breast cancer have resulted in incremental improvements on established methodologies.

Advances in Breast Cancer Detection Techniques: A Comprehensive Survey

Advances in Breast Cancer Detection Techniques: A Comprehensive Survey

THz radiation distribution for the identification of infiltrating ductal carcinoma in human breast model: a computational study

With numerous biomedical applications of terahertz (THz) imaging, spectroscopy, and sensing, the THz regime of the spectrum is quickly emerging as an important area of research with the potential to usher in a new era in the healthcare industry. In the medical field, THz radiation has been explored to diagnose and monitor several ailments, including foot diabetes, skin dryness, wounds, and burns. Compared to X-rays, THz waves' phase sensitivity to bodily fluid levels and water results in superior contrast and stronger absorption. This feature makes THz potentially useful for the diagnosis of various malignant tumor forms because cancerous tumors have higher water molecule contents than healthy tissues. The current study presents a computational analysis of a CW-THz point-by-point scanning technique for breast cancer detection. COMSOL multiphysics platform was utilized to create a multilayer three-dimensional breast model containing an irregular-shaped infiltrating ductal carcinoma in the glandular layer. The propagation of the THz radiation in the constructed breast model was simulated based on the radio frequency module and electromagnetic waves in the frequency domain interface. Furthermore, different volumes and locations of the induced tumor were examined. The results demonstrated significant disparities in the acquired electric distribution at different tumor sizes and sites between normal and diseased breasts. The resultant electric field for healthy breast ranged from 1.1 × 10–5 to 0.61 V/m, however, the electric field ranges following tumor addition fluctuate based on its volume and location. Additionally, the position of the THz source and detector array influences the reflected THz signal.

Low-Dose Positron Emission Mammography: A Novel, Promising Technique for Breast Cancer Detection.

Low-Dose Positron Emission Mammography: A Novel, Promising Technique for Breast Cancer Detection.

Mitigating Under-Sampling Artifacts in 3D Photoacoustic Imaging Using Res-UNet Based on Digital Breast Phantom.

In recent years, photoacoustic (PA) imaging has rapidly grown as a non-invasive screening technique for breast cancer detection using three-dimensional (3D) hemispherical arrays due to their large field of view. However, the development of breast imaging systems is hindered by a lack of patients and ground truth samples, as well as under-sampling problems caused by high costs. Most research related to solving these problems in the PA field were based on 2D transducer arrays or simple regular shape phantoms for 3D transducer arrays or images from other modalities. Therefore, we demonstrate an effective method for removing under-sampling artifacts based on deep neural network (DNN) to reconstruct high-quality PA images using numerical digital breast simulations. We constructed 3D digital breast phantoms based on human anatomical structures and physical properties, which were then subjected to 3D Monte-Carlo and K-wave acoustic simulations to mimic acoustic propagation for hemispherical transducer arrays. Finally, we applied a 3D delay-and-sum reconstruction algorithm and a Res-UNet network to achieve higher resolution on sparsely-sampled data. Our results indicate that when using a 757 nm laser with uniform intensity distribution illuminated on a numerical digital breast, the imaging depth can reach 3 cm with 0.25 mm spatial resolution. In addition, the proposed DNN can significantly enhance image quality by up to 78.4%, as measured by MS-SSIM, and reduce background artifacts by up to 19.0%, as measured by PSNR, even at an under-sampling ratio of 10%. The post-processing time for these improvements is only 0.6 s. This paper suggests a new 3D real time DNN method addressing the sparse sampling problem based on numerical digital breast simulations, this approach can also be applied to clinical data and accelerate the development of 3D photoacoustic hemispherical transducer arrays for early breast cancer diagnosis.

Comparative evaluation of data mining algorithms in breast cancer

<span lang="EN-US">Unchecked breast cell growth is one of the leading causes of death in women globally and is the cause of breast cancer. The only method to avoid breast cancer-related deaths is through early detection and treatment. The proper classification of malignancies is one of the most significant challenges in the medical industry. Due to their high precision and accuracy, machine learning techniques are extensively employed for identifying and classifying various forms of cancer. The authors of this review studied numerous data mining algorithms and implemented them such that clinicians might use them to accurately detect cancer cells early on. This article introduces several techniques, including support vector machine (SVM), K star (K*) classifier, additive regression (AR), back propagation (BP) neural network, and Bagging. These algorithms are trained using a set of data that contains tumor parameters from breast cancer patients. Comparing the results, the authors found that SVM and Bagging had the highest precision and accuracy, respectively. Also assess the number of studies that provide machine learning techniques for breast cancer detection.</span>

Microwave Imaging and Sensing Techniques for Breast Cancer Detection.

Medical imaging techniques, including X-ray mammography, ultrasound, and magnetic resonance imaging, play a crucial role in the timely identification and monitoring of breast cancer. However, these conventional imaging modalities have their limitations, and there is a need for a more accurate and sensitive alternative. Microwave imaging has emerged as a promising technique for breast cancer detection due to its non-ionizing, non-invasive, and cost-effective nature. Recent advancements in microwave imaging and sensing techniques have opened up new possibilities for the early diagnosis and treatment of breast cancer. By combining microwave sensing with machine learning techniques, microwave imaging approaches can rapidly and affordably identify and classify breast tumors. This manuscript provides a comprehensive overview of the latest developments in microwave imaging and sensing techniques for the early detection of breast cancer. It discusses the principles and applications of microwave imaging and highlights its advantages over conventional imaging modalities. The manuscript also delves into integrating machine learning algorithms to enhance the accuracy and efficiency of microwave imaging in breast cancer detection.

Computed diffusion-weighted imaging with a low-apparent diffusion coefficient-pixel cut-off technique for breast cancer detection.

This study aimed to compare the image quality and diagnostic performance of computed diffusion-weighted imaging (DWI) with low-apparent diffusion coefficient (ADC)-pixel cut-off technique (cDWI cut-off) and actual measured DWI (mDWI). Eighty-seven consecutive patients with malignant breast lesions and 72 with negative breast lesions who underwent breast MRI were retrospectively evaluated. Computed DWI with high b-values of 800, 1200, and 1500 s/mm2 and ADC cut-off thresholds of none, 0, 0.3, and 0.6 (×10-3 mm2/s) were generated from DWI with two b-values (0 and 800 s/mm2). To identify the optimal conditions, two radiologists evaluated the fat suppression and lesion reduction failure using a cut-off technique. The contrast between breast cancer and glandular tissue was evaluated using region of interest analysis. Three other board-certified radiologists independently assessed the optimised cDWI cut-off and mDWI data sets. Diagnostic performance was evaluated using receiver operating characteristic (ROC) analysis. When an ADC cut-off threshold of 0.3 or 0.6 (× 10-3 mm2/s) was applied, fat suppression improved significantly (p < .05). The contrast of the cDWI cut-off with a b-value of 1200 or 1500 s/mm2 was better than the mDWI (p < .01). The ROC area under the curve for breast cancer detection was 0.837 for the mDWI and 0.909 for the cDWI cut-off (p < .01). The cDWI cut-off provided better diagnostic performance than mDWI for breast cancer detection. Using the low-ADC-pixel cut-off technique, computed DWI can improve diagnostic performance by increasing contrast and eliminating un-suppressed fat signals.

Region-of-Interest Optimization for Deep-Learning-Based Breast Cancer Detection in Mammograms

The early detection and diagnosis of breast cancer may increase survival rates and reduce overall treatment costs. The cancer of the breast is a severe and potentially fatal disease that impacts individuals worldwide. Mammography is a widely utilized imaging technique for breast cancer surveillance and diagnosis. However, images produced with mammography frequently contain noise, poor contrast, and other anomalies that hinder radiologists from interpreting the images. This study develops a novel deep-learning technique for breast cancer detection using mammography images. The proposed procedure consists of two primary steps: region-of-interest (ROI) (1) extraction and (2) classification. At the beginning of the procedure, a YOLOX model is utilized to distinguish breast tissue from the background and to identify ROIs that may contain lesions. In the second phase, the EfficientNet or ConvNeXt model is applied to the data to identify benign or malignant ROIs. The proposed technique is validated using a large dataset of mammography images from various institutions and compared to several baseline methods. The pF1 index is used to measure the effectiveness of the technique, which aims to establish a balance between the number of false positives and false negatives, and is a harmonic mean of accuracy and recall. The proposed method outperformed existing methods by an average of 8.0%, obtaining superior levels of precision and sensitivity, and area under the receiver operating characteristics curve (ROC AUC) and the precision–recall curve (PR AUC). In addition, ablation research was conducted to investigate the effects of the procedure’s numerous components. According to the findings, the proposed technique is another choice that could enhance the detection and diagnosis of breast cancer using mammography images.

Generative adversarial networks with modified wasp swarm algorithm-based early-stage breast cancer detection techniques

Generative adversarial networks with modified wasp swarm algorithm-based early-stage breast cancer detection techniques

A Deep Learning Approach for Diagnosis Support in Breast Cancer Microwave Tomography.

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.

Machine Learning Research on Breast and Lung Cancer Detection

As the diagnosis of these cancer cells at late stages causes greater pain and raises the likelihood of death, the initial-state cancer finding is crucial to giving the patient the proper care and reducing the risk of dying from cancer. The publication offers a chance to research breast and lung cancer detection techniques as well as various algorithms for cancer early detection. With the aid of various image kinds and test results data sets, hybrid approaches are utilized to identify lung and breast cancer based on the size and form of the cells. The basic concept of breast and lung cancer block diagram is also explained in this study, with an emphasis on the difficulties and potential future applications of cancer detection and diagnosis techniques.

Machine Learning Research On Breast And Lung Cancer Detection

As the diagnosis of these cancer cells at late stages causes greater pain and raises the likelihood of death, the initial-state cancer finding is crucial to giving the patient the proper care and reducing the risk of dying from cancer. The publication offers a chance to research breast and lung cancer detection techniques as well as various algorithms for cancer early detection. With the aid of various image kinds and test results data sets, hybrid approaches are utilized to identify lung and breast cancer based on the size and form of the cells. The basic concept of breast and lung cancer block diagram is also explained in this study, with an emphasis on the difficulties and potential future applications of cancer detection and diagnosis techniques.

Experimental demonstration of ultrahigh sensitivity Talbot-Lau interferometer for low dose mammography

Objective. Even though the techniques used for breast cancer identification have advanced over the years, current mammography based on x-rays absorption, the ‘gold standard’ screening test at present, still has some shortcomings as concerns sensitivity and specificity to early-stage cancers, due to poor differentiation between tumor and normal tissues, especially in the case of the dense breasts. We investigate a possible additional technique for breast cancer detection with higher sensitivity and low dose, x-ray phase-contrast or refraction-based imaging with ultrahigh angular sensitivity grating interferometers, having several meters length. Approach. Towards this goal, we built and tested on a mammography phantom, a table-top laboratory setup based on a 5.7 m long Talbot-Lau interferometer with angular sensitivity better than 1 μrad. We used a high-power x-ray tungsten anode tube with a 400 μm focal spot, operated at 40 kVp and 15 mA with a 2 mm aluminum filter. Main results. The results reported in our paper confirm the ultrahigh sensitivity and dose economy possible with our setup. The visibility of objects simulating cancerous formations is strongly increased in the refraction images over the attenuation ones, even at a low dose of 0.32 mGy. Notably, the smallest fiber of 400 μm diameter and calcifications specs of 160 μm in diameter are detected, even though the spatial resolution at the object of our magnification M ∼ 2 setup with a 400 μm source spot is only ∼250 μm. Significance. Our experiments on a mammography phantom illustrate the capabilities of the proposed technique and can open the way toward low-dose interferometric mammography.

Indocyanine Green-Conjugated Superparamagnetic Iron Oxide Nanoworm for Multimodality Breast Cancer Imaging.

Breast cancer is the leading cause of cancer-associated deaths among women. Techniques for non-invasive breast cancer detection and imaging are urgently needed. Multimodality breast cancer imaging is attractive since it can integrate advantages from several modalities, enabling more accurate cancer detection. In order to accomplish this, indocyanine green (ICG)-conjugated superparamagnetic iron oxide nanoworm (NW-ICG) has been synthesized as a contrast agent. When evaluated in a spontaneous mouse breast cancer model, NW-ICG gave a large tumor to normal tissue contrasts in multiple imaging modalities including magnetic particle imaging, near-infrared fluorescence imaging, and photoacoustic imaging, providing more comprehensive detection and imaging of breast cancer. Thus, NW-ICGs are an attractive platform for non-invasive breast cancer diagnosis.

Machine learning and deep learning for breast cancer risk prediction and diagnosis: a Survey

Breast cancer is the widest spreading disease among women globally. The prevalence rate of breast cancer continued to rise in the last few decades. The mitotic count is a relevant factor for grading invasive breast cancer. Early analysis is an extremely imperative step in treatment. However, it is not an easy one due to several skepticisms in detection which employ mammograms. Since it is subject to human prone error, requires more time for completion and the nuclei look similar during all stages of mitosis, automatic detection of mitosis is a good solution to overcome these problems. Detailed analysis of breast cancer normally requires medical images of different methods. The sensitivity and specificity of the diagnosis largely depend on the experiences of the radiologists, and uncertain diagnosis is quite frequent because of resolution limitations and the concerns of lawsuits arisen from wrong diagnosis or undetected lesions. In this paper, the top methodologies used for mitosis detection are analyzed. There are many algorithms for classification and prediction of breast cancer: Support Vector Machine (SVM), Decision Tree (CART), k Nearest Neighbors (KNN), Random Forest (RF), and Bayesian Networks (BN). The Wisconsin data set was used to analyze breast cancer as a training set to assess and measure the performance of the three ML classifiers in terms of key frameworks such as accuracy, recall, precision, and ROC. The outcome obtained in this paper provides a critique of the stateofart ML techniques for breast cancer detection. It was also found that the ensemble classifier gives better performance. A preliminary experiment conducted on cascaded RF and Artificial Neural Network (ANN) results in better accuracy than individual classifiers. The paper shows how we can use deep learning technology diagnosis of breast cancer using MIAS Dataset. A deep learning approach is almost used for immense task objective Image processing, Computer Vision, Medical Diagnosis, and Neural Language Processing.

Breast cancer chemotherapy treatment monitoring based on serum sample Raman spectroscopy.

In this paper, breast cancer patients were monitored throughout their chemotherapy treatments (CHT), with blood serum sample Raman spectroscopy and multivariate analysis, approximately for a year. First of all, we discriminate between healthy and clinically diagnosed breast cancer patients. Breast cancer detection in terms of sensitivity and specificity were 87.14% and 90.55% respectively. Although no shifts of peaks in mean spectrum of samples from breast cancer patients were found with respect to the mean spectrum from control patients, some peaks did show clear differences in intensity, the greatest disparities found at 509, 545, 1063, 1103, 1338, 1556, 1083 and 1449 cm− 1 are associated with amino acids and phospholipid, 1246 and 1654 cm− 1, corresponding to amide III and I, respectively. Other peaks of interest encountered at 450, 661, 890, 917 and 1405 cm− 1 are associated to glutathione. Then, 6 breast cancer patients were monitored during their chemotherapy treatments, the results were in complete correspondence with their medical records, enabling a detailed study of the evolution of each patient’s cancer. A special interest arose in the possible correlation between the intensity of Raman peak, 450 cm− 1, corresponding to glutathione and evolution of cancer throughout CHT, i.e., glutathione appears to be a good candidate as breast cancer biomarker. The results confirmed that Raman spectroscopy and PCA are, not only a good support to current breast cancer detection techniques, but could also be excellent techniques to monitor more efficiently breast cancer patients undergoing CHT, using blood serum samples which are a lot less invasive than other methods.

Breast Tumor Detection and Classification Based on Microwave Imaging

Limitations caused by traditional breast cancer detection and screening techniques have encouraged researchers to investigate alternative solutions. This study examines the use of a microwave-based approach for tumor detection in breast tissue and related tumor type classification using matched-filtering. Radar-like confocal microwave imaging (CMI) method constructs the foundation of such tumor detection approach. In particular, a microwave pulse is first transmitted, then back-scattered pulses are collected. All major reflective sites in the breast tissue are detected by repeating this procedure on a microwave pulse transmission-reception grid, aligning captured signals in-time to focus on a particular region in the breast tissue and superimposing such time-shifted signals to improve signal-to-clutter level. In the observed signals, clutter is originated by the heterogeneity of the breast tissue while signal is originated by a tumor site as a function of its water content.&#x0D; All calculations, in the study, were performed computationally in terms of a 3D Finite-Difference Time-Domain (FDTD) simulation models. For the antenna system, two cross-polarized resistively loaded bow-ties antennas were used in the computational model, and the tumor site was modeled using five different size and morphologies. Matched-filtering, on the other hand, was performed matching such obtained observations with that of a homogenous breast tissue, namely clutter-free model. Performance of the proposed approach was tested for two different antenna array resolutions, and it was observed that this parameter is important for successful detection and classification of a tumor-site in a realistic heterogenous breast tissue model.

On the dielectric and mechanical characterization of tissue‐mimicking breast phantoms

Objective. In this paper, we focus on the dielectric and mechanical characterization of tissue-mimicking breast phantoms. Approach. Starting from recipes previously proposed by our research group, based on easy-to-handle, cheap and safe components (i.e. sunflower oil, deionized water, dishwashing liquid and gelatin), we produced and tested, both dielectrically and mechanically, more than 100 samples. The dielectric properties were measured from 500 MHz to 14 GHz, the Cole–Cole parameters were derived to describe the dielectric behaviour in a broader frequency range, and the results were compared with dielectric properties of human breast ex vivo tissues up to 50 GHz. The macroscale mechanical properties were measured by means of unconfined compression tests, and the impact of the experimental conditions (i.e. preload and test speed) on the measured Young’s moduli was analysed. In addition, the mechanical contrast between healthy- and malignant-tissue-like phantoms was evaluated. Main results. The results agree with the literature in the cases in which the experimental conditions are known, demonstrating the possibility to fabricate phantoms able to mimic both dielectric and mechanical properties of breast tissues. Significance. In this work, for the first time, a range of materials reproducing all the categories of breast tissues were experimentally characterized, both from a dielectric and mechanical point of view. A large range of frequency were considered for the dielectric measurements and several combinations of experimental conditions were investigated in the context of the mechanical characterization. The proposed results can be useful in the design and testing of complementary or supplementary techniques for breast cancer detection based on micro/millimetre-waves, possibly in connection with other imaging modalities.