Method For Detection Of Breast Cancer Research Articles

UPAYA PENINGKATAN SADARI UNTUK DETEKSI DINI KANKER PAYUDARA PADA REMAJA PUTRI

Breast cancer is a disease that threatens women's health, but early detection through the Self-Examination method can increase the chances of recovery. Young women at SMK Cendikia Husada Kotabumi are a group that is susceptible to breast cancer, but awareness of the importance of self-awareness is still low among them. This community service aims to increase the awareness and skills of Sadari in young women at SMK Cendikia Husada Kotabumi to support early detection of breast cancer. The community engagement approach was conducted through socialization, education, and training on the Sadari method. The activity was carried out collaboratively between the community engagement team and the school, involving 27 adolescent girls as participants. The results of the activity showed a significant improvement in the level of knowledge and understanding of the adolescent girls regarding the importance of Sadari as an early detection method for breast cancer, as evidenced by the percentage results before and after the activity. Prior to the activity, the majority of the participants had a low level of knowledge, with 21 individuals (77.8%), while after the activity, the majority of the participants showed good knowledge, with 25 individuals (92.6%). They also successfully developed skills in performing Sadari independently. Community service through outreach, counseling, and training on Realization is effective in increasing awareness and skills of young women in early detection of breast cancer. These steps need to be continued and extended to other schools and communities to protect women's health more broadly

Breast ultrasound image classification and physiological assessment based on GoogLeNet

BackgroundMedical ultrasound image classification based on convolutional neural network is the mainstream breast cancer classification model, but its limited perceptual ability limits its ability to obtain global information. MethodologyA total of 880 breast ultrasound images were collected from 700 patients including 103 normal images, 467 malignant tumor images and 210 benign tumor images. In this paper, the diagnosis of breast ultrasound images was realized by constructing the CNN model of GoogLeNet. Firstly, the breast images were preprocessed based on the TV model. After that, the CNN model was trained and a more accurate model with a wider range of application was obtained based on improved Inception. Then we extract image features of different sizes; Then, feature classification was completed in different model classifiers to realize benign and malignant detection of breast cancer. Meanwhile, comparative analysis was performed to verify the excellence of the GoogLeNet model. ResultsThe training time for classification was effectively reduced, and the classification accuracy rate was improved, reaching 96.37% combined with transformer learning. The loss value is down to 0.3492. Then, the classification accuracy of different structural models is discussed in the two models. The results show that GoogLeNet has great advantages in the detection of breast cancer ultrasound images. The influence of migration on experimental results is further discussed. Finally, combined with transfer learning, the three models were tested separately. The results show that transfer learning can improve system performance. ConclusionBased on the TV model and the GoogLeNet model, this paper designs a method of breast cancer detection and classification based on the combination of TV and GoogLeNet model and transfer learning. Experiments show that this method can repair part of the texture damaged by markers in ultrasonic images, effectively extract the features of thyroid nodule, and accurately judge whether the breast is diseased, which greatly improves the diagnostic efficiency of doctors.

Beyond a decade: a comparative study of 15-year survival rates in screen-detected vs. symptomatic breast cancer patients in Hungary

BackgroundBreast cancer is the leading cancer in women globally. Despite decreasing mortality rates, largely due to early detection and modern treatment, the effectiveness of screening on long-term survival outcomes remains unclear.AimsThis study evaluates the 15-year survival outcomes of a national breast cancer screening program initiated in Hungary in 2002.MethodsUsing a prospectively maintained patient database, the study included individuals from the first 6 years of the program who underwent surgery for histologically confirmed breast cancer and had available follow-up information. Patients were categorized based on the method of breast cancer detection into two groups: those diagnosed during or 2 years after the population-based screening exam (Group A), and those who self-detected or sought medical attention for symptoms (Group B).ResultsOf the 309 patients who underwent breast cancer surgery, 208 were screen-detected (Group A) and 101 were symptomatic (Group B). The 15-year overall survival was 75.0% for Group A and 76.2% for Group B (p = 0.927). The 15-year disease-specific survival was 85.6% and 81.2% (p = 0.249), respectively. A statistically not significant positive trend in disease-free survival was observed in Group A (81.7% vs. 75.2%; p = 0.144).ConclusionsThe study underscores the importance of extended follow-up periods in evaluating the outcomes of breast cancer screening programs. While the screening program may not significantly enhance overall survival rates, it has demonstrated a reduction in the mastectomy rate and could potentially extend periods of disease-free survival. These findings contribute to the ongoing discourse about the long-term benefits of breast cancer screening programs.

Awareness and Practice of Breast Self-Examination among Female Undergraduate Students

Breast cancer is a prevalent form of cancer and the second leading cause of cancer-related deaths in women across the globe. Women can perform breast self-examination at home to detect any abnormalities in the breast early on, making it an affordable, easy, and effective screening technique. In this cohort study, we aimed to assess undergraduates' knowledge and practice of breast self-examination. Between February 2023 and May 2023, we used an online survey tool to collect information and structured the questions to meet our research objectives. Findings indicate that 40% of those recruited were knowledgeable about the procedure, while 60% had minimal or no knowledge. Age did not significantly impact knowledge and practice (p=0.7). Women, especially female students, must be more informed about breast cancer and encouraged to perform breast self-examination regularly. Raising awareness can be achieved through organizing workshops on breast cancer and early detection methods on university campuses, secondary schools, and even in public markets. Pamphlets may also be an effective method for promoting awareness.

Classifications of benign and malignant mammogram images using Gabor‐modified CNN architecture

AbstractBreast cancer is one of the leading life killing cancers in women patients around the world. Digital mammogram is used to detect and segment the abnormal mass portions in breast region. In this article, the breast cancer regions are detected and segmented using the Gabor transform based Convolutional Neural Networks (CNN) classification approach. This proposed breast cancer detection method consist of the following modules Kirsch's edge detector, Gabor transform, CNN classification and Segmentation. The cancer pixels and healthy pixels are differentiated in edge pixels. Hence, the Kirsch's edge detector is used to detect the fine edge boundary pixelsthe in source mammogram image. Then, the spatial edge detected mammogram image is converted into multi resolution mammogram image using Gabor transform. This transformed multi resolution mammogram image is classified into Normal, Benign or Malignant in this article using the proposed CNN architecture. The developed CNN structure extracts more complex feature maps from their internal layers to improve the classification rate. The conventional and proposed CNN structure is differed with respect to the internal layers. The proposed CNN structure is optimized by reducing the usage of the internals and increases Convolutional Filters (CF) in each Convolutional Layer during utilization design process. After classification process over, the cancer regions in the abnormal mammogram images are segmented using morphological functions. The proposed breast cancer detection is evaluated on Mammographic Image Analysis Society (MIAS) dataset and their cancer region segmentation results are 98.4% of sensitivity, 98.9% of specificity and 99.2% of cancer region segmentation accuracy. The average Detection Rate (DR) of the proposed breast cancer detection method is about 98.3% on the set of mammogramthe images from MIAS dataset. These simulation results are compared with other state‐of‐the art methods and cross verified by the k‐fold verification algorithm.

MRI-Based Breast Cancer Classification and Localization by Multiparametric Feature Extraction and Combination Using Deep Learning.

Deep learning (DL) have been reported feasible in breast MRI. However, the effectiveness of DL method in mpMRI combinations for breast cancer detection has not been well investigated. To implement a DL method for breast cancer classification and detection using feature extraction and combination from multiple sequences. Retrospective. A total of 569 local cases as internal cohort (50.2 ± 11.2 years; 100% female), divided among training (218), validation (73) and testing (278); 125 cases from a public dataset as the external cohort (53.6 ± 11.5 years; 100% female). T1-weighted imaging and dynamic contrast-enhanced MRI (DCE-MRI) with gradient echo sequences, T2-weighted imaging (T2WI) with spin-echo sequences, diffusion-weighted imaging with single-shot echo-planar sequence and at 1.5-T. A convolutional neural network and long short-term memory cascaded network was implemented for lesion classification with histopathology as the ground truth for malignant and benign categories and contralateral breasts as healthy category in internal/external cohorts. BI-RADS categories were assessed by three independent radiologists as comparison, and class activation map was employed for lesion localization in internal cohort. The classification and localization performances were assessed with DCE-MRI and non-DCE sequences, respectively. Sensitivity, specificity, area under the curve (AUC), DeLong test, and Cohen's kappa for lesion classification. Sensitivity and mean squared error for localization. A P-value <0.05 was considered statistically significant. With the optimized mpMRI combinations, the lesion classification achieved an AUC = 0.98/0.91, sensitivity = 0.96/0.83 in the internal/external cohorts, respectively. Without DCE-MRI, the DL-based method was superior to radiologists' readings (AUC 0.96 vs. 0.90). The lesion localization achieved sensitivities of 0.97/0.93 with DCE-MRI/T2WI alone, respectively. The DL method achieved high accuracy for lesion detection in the internal/external cohorts. The classification performance with a contrast agent-free combination is comparable to DCE-MRI alone and the radiologists' reading in AUC and sensitivity. 3. Stage 2.

Review of Breast Cancer Detection Method

The fierceness of breast cancer is obvious for the patients and the field of medical studies for its malignant destruction in the human body. The failure in diagnosis may put patients in suffering from high financial pressure and may cause the patients’ physical health to deteriorate. The key in this research of contrasting different kinds of algorithms and varied types of classifications is to support physicians to make a better judgment to prevent misdiagnosis. The research introduced many aspects of data on breast cancer detection to make the data inputs of algorithms able to satisfy doctors’ needs. Moreover, the varied kinds of features will specify and confirm the presence of breast cancer tumours instead of treating them as a part of the benign tissues. With the information above, the research found a difference in accuracy between the classification and tried to make a comparison to conclude that most satisfy and support the actual use of these data to physicians, who may see the symptoms with misunderstanding. Thus, Random Forest was qualified to be evaluated as the most applicable algorithm of its excellence in processing higher dimensional, which is dynamic, data.

Applying Deep Learning Methods for Mammography Analysis and Breast Cancer Detection

Breast cancer is a serious medical condition that requires early detection for successful treatment. Mammography is a commonly used imaging technique for breast cancer screening, but its analysis can be time-consuming and subjective. This study explores the use of deep learning-based methods for mammogram analysis, with a focus on improving the performance of the analysis process. The study is focused on applying different computer vision models, with both CNN and ViT architectures, on a publicly available dataset. The innovative approach is represented by the data augmentation technique based on synthetic images, which are generated to improve the performance of the models. The results of the study demonstrate the importance of data pre-processing and augmentation techniques for achieving high classification performance. Additionally, the study utilizes explainable AI techniques, such as class activation maps and centered bounding boxes, to better understand the models’ decision-making process.

BCDNet: An Optimized Deep Network for Ultrasound Breast Cancer Detection

ObjectivesBreast cancer is a common but deadly disease among women. Medical imaging is an effective method to diagnose breast cancer, but manual image screening is time-consuming. In this study, a novel computer-aided diagnosis system for breast cancer detection called BCDNet is proposed. Material and MethodsWe leverage pre-trained convolutional neural networks (CNNs) for representation learning and propose an adaptive backbone selection algorithm to obtain the best CNN model. An extreme learning machine serves as the classifier in the BCDNet, and a bat algorithm with chaotic maps is put forward to further optimize the parameters in the classifiers. A public ultrasound image dataset is used in the experiments based on 5-fold cross-validation. ResultsSimulation results suggest that our BCDNet outperforms several state-of-the-art breast cancer detection methods in terms of accuracy. ConclusionThe proposed BCDNet is a useful auxiliary tool that can be applied in clinical screening for breast cancer.

Abstract P1-05-21: Change in breast cancer detection method, stage at diagnosis and treatment during the COVID-19 Pandemic: 2019-2021

Abstract Objective: Identify changes in breast cancer detection method, stage at diagnosis and treatment prior to, during and after stay-at-home orders and restricted health care access due to COVID-19. Methods: Statistical comparison of detection method (patient (PtD), mammography (MamD) or other), Anatomic TNM Stage 8 (0-IV) and invasive BC treatment change over time by three time periods (time 1: 2019+Q1 2020; time 2: Q2-Q4 2020; time 3: 2021) using chi-square analysis in an institutional retrospective cohort of first primary breast cancer (BC) patients (n=1799), years 2019-2021. Results: In the years prior to the study, 2016-2019, there was no difference in detection method or stage at diagnosis by year with 682 to 733 newly diagnosed BC annually (p=.462). In 2020 (n=535) and 2021 (n=582) annual diagnosed cases dropped 22% and 15% from 2019 levels. Compared to time 1, time 2 MamD BC dropped significantly (64% to 58%) with a subsequent increase in MamD BC to 70% in time 3 (p &amp;lt; .001) creating a U-shaped curve for MamD over time. PtD BC increased in time 2 from 30% to 36% but declined in time 3 to 25% (p &amp;lt;.001). Concurrently, stage at diagnosis shifted from time 1 to time 2 with stage 0 and I declining [stage 0: 21% to 16%, stage I: 40% to 38%] and stage II and IV increasing [stage II: 28% to 33%, stage IV: 2% to 4% stage IV] (p&amp;lt;.001). Subsequently in 2021 stage shifted again with an increase in stage 0 to 22% and stage I to 45% and a decline in stage II (33% to 24%), III (9% to 7%) and IV (4% to 2%) (p&amp;lt;.001). Combining stage 0 and I, the percentage of lower stage BC declined from 61% to 54% and increased to 67% in time 3 (2021) when health services became more readily accessible. There was no change in type of surgery for invasive breast cancer (stage I-III, n=1386) with equivalent numbers of breast conserving surgery (58%), subcutaneous mastectomy (24%) and mastectomy (18%) over the time period. Chemotherapy treatment rates for invasive BC did not change (38%). Radiation therapy increased from 66% (time 1: 2019+Q1 2020) to 73% (time 2: Q2-Q4 2020) then back to 64% in time 3: 2021 (p=.007) independent of surgery type but concordant with an increase in stage IA and stage IIB BC among invasive breast cancer cases in time 2: Q2-Q4 2020 (p&amp;lt;.001). Likewise, neoadjuvant therapy increased and then declined from 33% to 38% to 29% from time 1 to time 3 (p&amp;lt;.001). Conclusions: Number of diagnosed BC cases fell after the first quarter of 2020 during the time of COVID-19 related shut downs and decreased access to health services. During the Q2-Q4 2020 time period mammography detected BC declined with a relative increase in patient detected breast cancer. When mammography detection declined, BC stage at diagnosis shifted to higher stage concurrent with increased rates of radiation and neoadjuvant therapy. In 2021, the relative increase in mammography detected BC indicates a return to more normal screening patterns with a catch up for screening lost in the prior year due to access limitations. In the third time period: 2021, with the return to prior levels of mammography detected breast cancer, stage shifted back to pre-pandemic expected distribution and the excess treatment with radiation and neoadjuvant therapy declined to previously observed levels. Although the changes in detection method, stage and treatment did not persist they were statistically significant and could represent a need for re-establishing pre-pandemic screening behavior. Table 1 Detection Method by COVID Pandemic Diagnosis Time Periods: 2019-2021 (N=1799). Citation Format: Judith A. Malmgren, Mary Atwood, Henry Kaplan. Change in breast cancer detection method, stage at diagnosis and treatment during the COVID-19 Pandemic: 2019-2021 [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P1-05-21.

Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction

Breast cancer is one of the precarious conditions that affect women, and a substantive cure has not yet been discovered for it. With the advent of Artificial intelligence (AI), recently, deep learning techniques have been used effectively in breast cancer detection, facilitating early diagnosis and therefore increasing the chances of patients' survival. Compared to classical machine learning techniques, deep learning requires less human intervention for similar feature extraction. This study presents a systematic literature review on the deep learning-based methods for breast cancer detection that can guide practitioners and researchers in understanding the challenges and new trends in the field. Particularly, different deep learning-based methods for breast cancer detection are investigated, focusing on the genomics and histopathological imaging data. The study specifically adopts the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), which offer a detailed analysis and synthesis of the published articles. Several studies were searched and gathered, and after the eligibility screening and quality evaluation, 98 articles were identified. The results of the review indicated that the Convolutional Neural Network (CNN) is the most accurate and extensively used model for breast cancer detection, and the accuracy metrics are the most popular method used for performance evaluation. Moreover, datasets utilized for breast cancer detection and the evaluation metrics are also studied. Finally, the challenges and future research direction in breast cancer detection based on deep learning models are also investigated to help researchers and practitioners acquire in-depth knowledge of and insight into the area.

Performance Analysis of Breast Cancer Detection Method Using ANFIS Classification Approach

Breast cancer is one of the deadly diseases prevailing in women. Earlier detection and diagnosis might prevent the death rate. Effective diagnosis of breast cancer remains a significant challenge, and early diagnosis is essential to avoid the most severe manifestations of the disease. The existing systems have computational complexity and classification accuracy problems over various breast cancer databases. In order to overcome the above-mentioned issues, this work introduces an efficient classification and segmentation process. Hence, there is a requirement for developing a fully automatic methodology for screening the cancer regions. This paper develops a fully automated method for breast cancer detection and segmentation utilizing Adaptive Neuro Fuzzy Inference System (ANFIS) classification technique. This proposed technique comprises preprocessing, feature extraction, classifications, and segmentation stages. Here, the wavelet-based enhancement method has been employed as the preprocessing method. The texture and statistical features have been extracted from the enhanced image. Then, the ANFIS classification algorithm is used to classify the mammogram image into normal, benign, and malignant cases. Then, morphological processing is performed on malignant mammogram images to segment cancer regions. Performance analysis and comparisons are made with conventional methods. The experimental result proves that the proposed ANFIS algorithm provides better classification performance in terms of higher accuracy than the existing algorithms.

Neutrosophic-based Machine Learning Techniques for Analysis and Diagnosis the Breast Cancer

Approximately one in eight women will get breast cancer in their lifetime. Because of the risks associated with radiation exposure, various women choose to avoid getting detected with breast cancer. Non-invasive breast cancer detection methods have limitations concerning the safety of radiation exposure and the accuracy with which tumors in the breast are diagnosed. Machine learning methods are commonly used to diagnose breast cancer. This paper applied three different machine learning methods like KNN, Naïve Bayes, and ID3. These methods are applied to the Wisconsin Breast Cancer dataset. In the process of categorization, data with unbalanced classes is problematic because methods are more probable to categorize fresh observations to the majority class since the likelihood of cases forming the plurality class is considerably high. So neutrosophic set is used to overcome the vague and uncertain data. This paper used single-valued neutrosophic numbers to evaluate the criteria. This paper used ROC and accuracy to evaluate the methods. The KNN has a 96.7%, Naïve Bayes has a 95.2%, and ID3 has a 95.3% accuracy.

Fast Watershed Segmentation for Breast Cancer Detection

Within engineering and computer specializations, image processing is the most important study topic. It is one of today's fastest-growing technologies, with applications in a variety of biological sectors, including cancer sickness. According to the latest data from throughout the world, breast cancer is the most lethal of all cancer kinds. It is the most frequent cancer in women and the second leading cause of cancer mortality in women. In this study, we advocate using a watershed transformation to create a fast segmentation technique. This allows for the blending of updated information about picture objects, extending the partitioning of the dividing waterline and therefore the standard watershed technique. The method requires a mechanism to express the test picture in terms of the amount of change around every given pixel before it can begin the watershed modification. Each pixel in the greyscale representation of the original picture is subjected to the Sobel operator. According to the form, the tumors identified are round or semicircular and the light of the tumor dims as we travel away from its core. The complement for this previous data may be seen as a local minimum that necessitated the start of the watershed process. As a result, each tumor picture may be represented as a lake, with the center in the complement tumor picture being the least value. The identification of tumor percentage gets more reliable after using the approach. As a consequence, our computer-aided diagnostic method for mammographic breast cancer detection has improved significantly thanks to the novel methodology. The method was written in MATLAB and tested on a Windows computer. The strategy was put to the test using photos from MIAS (Mammogram Image Analysis Society, UK), which offers a consistent categorization system for mammographic examinations. In this study, we advocate using a watershed transformation to create a rapid segmentation technique. This allows for the blending of data about picture objects, extending the partitioning of the dividing waterline and therefore the standard watershed technique.

Breast Cancer Detection in Thermography Using Convolutional Neural Networks (CNNs) with Deep Attention Mechanisms

Breast cancer is one of the most common types of cancer among women. Accurate diagnosis at an early stage can reduce the mortality associated with this disease. Governments and health organizations stress the importance of early detection of breast cancer as it is related to an increase in the number of available treatment options and increased survival. Early detection gives patients the best chance of receiving effective treatment. Different types of images and imaging modalities are used in the detection and diagnosis of breast cancer. One of the imaging types is “infrared thermal” breast imaging, where a screening instrument is used to measure the temperature distribution of breast tissue. Although it has not been used often, compared to mammograms, it showed promising results when used for early detection. It also has many advantages as it is non-invasive, safe, painless, and inexpensive. The literature has indicated that the use of thermal images with deep neural networks improves the accuracy of early diagnosis of breast malformation. Therefore, in this paper, we aim to investigate to what extent convolutional neural networks (CNNs) with attention mechanisms (AMs) can provide satisfactory detection results in thermal breast cancer images. We present a model for breast cancer detection based on deep neural networks with AMs using thermal images from the Database for Research Mastology with Infrared Image (DMR-IR). The model will be evaluated in terms of accuracy, sensitivity and specificity, and will be compared against state-of-the-art breast cancer detection methods. The AMs with the CNN model achieved encouraging test accuracy rates of 99.46%, 99.37%, and 99.30% on the breast thermal dataset. The test accuracy of CNNs without AMs was 92.32%, whereas CNNs with AMs achieved an improvement in accuracy of 7%. Moreover, the proposed models outperformed previous models that were reviewed in the literature.

Heat transfer capacity in millimeter size breast cancer cells analysis through thermal imaging and FDNCNN for primary stage identification

In this paper, cancer cells in human breast of below 256 cell (2 cm) counts are detected through proposed Feature DnCNN (FDnCNN) enhancement algorithm. Cancer cell count below 256 is termed as T1b and T1c stages. FDnCNN is applied to the breast thermal images obtained after external heating of breast phantom through warm water. Induced heat due to warm water immersion of breast phantom helps for detection of the small size cancer cell through heat transfer capacity between normal and cancer cells. However, traditional method of breast cancer detection through thermal imaging never detects the cancer cell size of below 2 cm (256-cell) in the images. Thermal imaging breast cancer detects cancer cell of above 256 cells count in breast and never below cancer cell of count 256 cells. The proposed method of acquiring the thermal image after induced heating on the breast phantom, leads to higher interpretation of cancer cells below 256 cell count because of increase in thermogenesis of tissue. The increase in thermogenesis in cancer tissues improves visibility of cancer cell below 256 cell count. Moreover, induced heat based phantom thermal images need enhancement algorithm for better visualization of cancer cell count of below 256. The proposed FDnCNN enhancement algorithm performs better than traditional algorithms and provides an accuracy of cancer cell measurement of about 95%, when compared to ground truth measurement of cancer cells.

Improving breast cancer diagnosis by incorporating raw ultrasound parameters into machine learning

The improved diagnostic accuracy of ultrasound breast examinations remains an important goal. In this study, we propose a biophysical feature-based machine learning method for breast cancer detection to improve the performance beyond a benchmark deep learning algorithm and to furthermore provide a color overlay visual map of the probability of malignancy within a lesion. This overall framework is termed disease-specific imaging. Previously, 150 breast lesions were segmented and classified utilizing a modified fully convolutional network and a modified GoogLeNet, respectively. In this study multiparametric analysis was performed within the contoured lesions. Features were extracted from ultrasound radiofrequency, envelope, and log-compressed data based on biophysical and morphological models. The support vector machine with a Gaussian kernel constructed a nonlinear hyperplane, and we calculated the distance between the hyperplane and each feature’s data point in multiparametric space. The distance can quantitatively assess a lesion and suggest the probability of malignancy that is color-coded and overlaid onto B-mode images. Training and evaluation were performed on in vivo patient data. The overall accuracy for the most common types and sizes of breast lesions in our study exceeded 98.0% for classification and 0.98 for an area under the receiver operating characteristic curve, which is more precise than the performance of radiologists and a deep learning system. Further, the correlation between the probability and Breast Imaging Reporting and Data System enables a quantitative guideline to predict breast cancer. Therefore, we anticipate that the proposed framework can help radiologists achieve more accurate and convenient breast cancer classification and detection.

Breast cancer early detection by using Fourier-transform infrared spectroscopy combined with different classification algorithms

Early detection of breast cancer is of great value in improving the prognosis. The current detection methods of breast cancer have their own limitations. In this study, we investigated the feasibility of Fourier Transform Infrared (FT-IR) spectroscopy combined with different classification algorithms for the early detection of breast cancer in a large sample of 526 patients, including 308 invasive breast cancer, 101 ductal carcinoma in situ, and 117 healthy controls. The serum was measured with FT-IR spectroscopy. Kennard-Stone (KS) algorithm was used to divide the data into the training set and testing set. Support vector machine (SVM) model and back propagation neural network (BPNN) model were used to distinguish ductal carcinoma in situ, invasive breast cancer from healthy controls. The accuracies of the SVM model and BPNN model were 92.9% and 94.2%. To determine the effect of different material absorption bands on early detection, the band was divided into four parts including 900–1425 cm−1, 1475–1710 cm−1, 2800–3000 cm−1, and 3090–3700 cm−1, to be modeled and detected respectively. The final results showed that the ranges 900–1425 cm−1 and 1475–1710 cm−1 had superior classification accuracies. The region 900–1425 cm−1 corresponded to the lipids, proteins, sugar, and nucleic acids, and the region 1475–1710 cm−1 corresponded to the proteins. The biochemical substances in other bands also contributed some unique potential to the classification, so the classification accuracy was the best in the full band. The study indicates that serum FT-IR spectroscopy combined with SVM and BPNN models is an effective tool for the early detection of breast cancer.

A Novel Hybrid Deep Learning Model for Metastatic Cancer Detection.

Cancer has been found as a heterogeneous disease with various subtypes and aims to destroy the body's normal cells abruptly. As a result, it is essential to detect and prognosis the distinct type of cancer since they may help cancer survivors with treatment in the early stage. It must also divide cancer patients into high- and low-risk groups. While realizing efficient detection of cancer is frequently a time-taking and exhausting task with the high possibility of pathologist errors and previous studies employed data mining and machine learning (ML) techniques to identify cancer, these strategies rely on handcrafted feature extraction techniques that result in incorrect classification. On the contrary, deep learning (DL) is robust in feature extraction and has recently been widely used for classification and detection purposes. This research implemented a novel hybrid AlexNet-gated recurrent unit (AlexNet-GRU) model for the lymph node (LN) breast cancer detection and classification. We have used a well-known Kaggle (PCam) data set to classify LN cancer samples. This study is tested and compared among three models: convolutional neural network GRU (CNN-GRU), CNN long short-term memory (CNN-LSTM), and the proposed AlexNet-GRU. The experimental results indicated that the performance metrics accuracy, precision, sensitivity, and specificity (99.50%, 98.10%, 98.90%, and 97.50) of the proposed model can reduce the pathologist errors that occur during the diagnosis process of incorrect classification and significantly better performance than CNN-GRU and CNN-LSTM models. The proposed model is compared with other recent ML/DL algorithms to analyze the model's efficiency, which reveals that the proposed AlexNet-GRU model is computationally efficient. Also, the proposed model presents its superiority over state-of-the-art methods for LN breast cancer detection and classification.

A low cost and efficient breast cancer detection method with a staircase shaped ultrawide band dielectric resonator antenna using monostatic radar based microwave imaging technique

In this article, a staircase-shaped ultra-wide band dielectric resonator antenna (DRA) has been used as a sensor for the detection of breast tumor by monostatic radar-based microwave imaging (MRMWI). The proposed DRA has fractional bandwidth (BW) 98.5% and a high peak gain 5.98 dB along with dual polarization behaviour from 5.12 to 8.2 GHz and 11.02-13.8 GHz. In the MRMWI setup, DRA is placed over the breast phantom at a distance of 7 mm and provides a safe exposure of radiation (<1.6 W/Kg). For simulation, it rotates around the phantom at a fixed interval in elevation (0-180°) and azimuthal (0-360°) planes. It works as a radiate and receives the reflected signals towards and from the scanned area simultaneously. To validate the results, fabricated DRA is connected to a vector network analyzer and rotates (as done for simulation) around the artificial breast phantom. That is a replica of the human breast made from gelatin+sugar, Vaseline and wheat flour+water equivalent to skin, fat and tumor respectively. Afterward S11 responses are recorded in the presence and absence of tumor inside the phantom. A significant variation in recorded values leads to the detection of tumors that are processed further in beam-forming algorithms; delay and sum (DAS) and delay-multiply and sum (DMAS) to reform the 2-dimensional image of tumor in MATLAB.