scholarly journals Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network

2022 ◽  
Vol 2022 ◽  
pp. 1-16
Author(s):  
Aqsa Mohiyuddin ◽  
Asma Basharat ◽  
Usman Ghani ◽  
Veselý Peter ◽  
Sidra Abbas ◽  
...  
Keyword(s):  
Breast Tumor ◽  
Breast Imaging ◽  
Breast Cancer Incidence ◽  
False Negative ◽  
Breast Tumors ◽  
Batch Size ◽  
Positive Ratio ◽  
False Positive Ratio ◽  
Proposed Model ◽  
Image Enhancing

Breast cancer incidence has been rising steadily during the past few decades. It is the second leading cause of death in women. If it is diagnosed early, there is a good possibility of recovery. Mammography is proven to be an excellent screening technique for breast tumor diagnosis, but its detection and classification in mammograms remain a significant challenge. Previous studies’ major limitation is an increase in false positive ratio (FPR) and false negative ratio (FNR), as well as a drop in Matthews correlation coefficient (MCC) value. A model that can lower FPR and FNR while increasing MCC value is required. To overcome prior research limitations, a modified network of YOLOv5 is used in this study to detect and classify breast tumors. Our research is conducted using publicly available datasets Curated Breast Imaging Subset of DDSM (CBIS-DDSM). The first step is to perform preprocessing, which includes image enhancing techniques and the removal of pectoral muscles and labels. The dataset is then annotated, augmented, and divided into 60% for training, 30% for validation, and 10% for testing. The experiment is then performed using a batch size of 8, a learning rate of 0.01, a momentum of 0.843, and an epoch value of 300. To evaluate the performance of our proposed model, our proposed model is compared with YOLOv3 and faster RCNN. The results show that our proposed model performs better than YOLOv3 and faster RCNN with 96% mAP, 93.50% MCC value, 96.50% accuracy, 0.04 FPR, and 0.03 FNR value. The results show that our suggested model successfully identifies and classifies breast tumors while also overcoming previous research limitations by lowering the FPR and FNR and boosting the MCC value.

scholarly journals Quality Assessment of Tire Shearography Images via Ensemble Hybrid Faster Region-Based ConvNets

Electronics ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 45 ◽  
Author(s):  
Chuan-Yu Chang ◽  
Kathiravan Srinivasan ◽  
Wei-Chun Wang ◽  
Ganapathy Pattukandan Ganapathy ◽  
Durai Raj Vincent ◽  
...  
Keyword(s):  
Neural Networks ◽  
Quality Assessment ◽  
Convolutional Neural Networks ◽  
False Positive ◽  
High Performance ◽  
Learning Approaches ◽  
Positive Ratio ◽  
False Positive Ratio ◽  
Proposed Model ◽  
Human Eyes

In recent times, the application of enabling technologies such as digital shearography combined with deep learning approaches in the smart quality assessment of tires, which leads to intelligent tire manufacturing practices with automated defects detection. Digital shearography is a prominent approach that can be employed for identifying the defects in tires, usually not visible to human eyes. In this research, the bubble defects in tire shearography images are detected using a unique ensemble hybrid amalgamation of the convolutional neural networks/ConvNets with high-performance Faster Region-based convolutional neural networks. It can be noticed that the routine of region-proposal generation along with object detection is accomplished using the ConvNets. Primarily, the sliding window based ConvNets are utilized in the proposed model for dividing the input shearography images into regions, in order to identify the bubble defects. Subsequently, this is followed by implementing the Faster Region-based ConvNets for identifying the bubble defects in the tire shearography images and further, it also helps to minimize the false-positive ratio (sometimes referred to as the false alarm ratio). Moreover, it is evident from the experimental results that the proposed hybrid model offers a cent percent detection of bubble defects in the tire shearography images. Also, it can be witnessed that the false-positive ratio gets minimized to 18 percent.

Limits of the ‘Mini-Mental State’ as a screening test for dementia and delirium among hospital patients

1982 ◽  
Vol 12 (2) ◽  
pp. 397-408 ◽  
Author(s):  
James C. Anthony ◽  
Linda LeResche ◽  
Unaiza Niaz ◽  
Michael R. Von Korff ◽  
Marshal F. Folstein
Keyword(s):  
Mental State ◽  
False Positive ◽  
Mini Mental State Examination ◽  
False Negative ◽  
General Medical Ward ◽  
Positive Ratio ◽  
False Positive Ratio ◽  
Hospital Patients ◽  
Screen Instrument ◽  
State Examination

SynopsisWith a psychiatrist's standardized clinical diagnosis as the criterion, the ‘Mini-Mental State’ Examination (MMSE) was 87% sensitive and 82% specific in detecting dementia and delirium among hospital patients on a general medical ward. The false positive ratio was 39% and the false negative ratio was 5 %. All false positives had less than 9 years of education; many were 60 years of age or older. Performance on specific MMSE items was related to education or age. These findings confirm the MMSE's value as a screen instrument for dementia and delirium when later, more intensive diagnostic enquiry is possible; they reinforce earlier suggestions that the MMSE alone cannot yield a diagnosis for these conditions.

scholarly journals Microwave Breast Imaging Using Rotational Bistatic Impulse Radar for the Detection of Breast Cancer: Protocol for a Prospective Diagnostic Study (Preprint)

2019 ◽  
Author(s):  
Shinsuke Sasada ◽  
Norio Masumoto ◽  
Hang Song ◽  
Akiko Emi ◽  
Takayuki Kadoya ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Diagnostic Accuracy ◽  
Breast Imaging ◽  
Molecular Subtype ◽  
Breast Tumors ◽  
Cancer Surveillance ◽  
Benign Tumors ◽  
Diagnostic Study ◽  
Imaging Device ◽  
Microwave Breast Imaging

BACKGROUND Mammography is the standard examination for breast cancer screening; however, it is associated with pain and exposure to ionizing radiation. Microwave breast imaging is a less invasive method for breast cancer surveillance. A bistatic impulse radar–based breast cancer detector has recently been developed. OBJECTIVE This study aims to present a protocol for evaluating the diagnostic accuracy of the novel microwave breast imaging device. METHODS This is a prospective diagnostic study. A total of 120 participants were recruited before treatment administration and divided into 2 cohorts: 100 patients diagnosed with breast cancer and 20 participants with benign breast tumors. The detector will be directly placed on each breast, while the participant is in supine position, without a coupling medium. Confocal images will be created based on the analyzed data, and the presence of breast tumors will be assessed. The primary endpoint will be the diagnostic accuracy, sensitivity, and specificity of the detector for breast cancer and benign tumors. The secondary endpoint will be the safety and detectability of each molecular subtype of breast cancer. For an exploratory endpoint, the influence of breast density and tumor size on tumor detection will be investigated. RESULTS Recruitment began in November 2018 and was completed by March 2020. We anticipate the preliminary results to be available by summer 2021. CONCLUSIONS This study will provide insights on the diagnostic accuracy of microwave breast imaging using a rotational bistatic impulse radar. The collected data will improve the diagnostic algorithm of microwave imaging and lead to enhanced device performance. CLINICALTRIAL Japan Registry of Clinical Trials jRCTs062180005; https://jrct.niph.go.jp/en-latest-detail/jRCTs062180005 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/17524

Breast Tumor Detection Via Fuzzy Morphological Operations

2019 ◽  
Vol 11 (1) ◽  
pp. 33-44
Author(s):  
Mohammed Y. Kamil ◽  
Ali Mohammed Salih
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Breast Tumor ◽  
Early Detection Of Cancer ◽  
Breast Tumors ◽  
Morphological Operations ◽  
Key Factors ◽  
Fuzzy Morphology ◽  
High Flexibility ◽  
Statistical Results

Breast cancer is one of most dangerous diseases and more common in women. The early detection of cancer is one of the most key factors for possible cure. There are numerous methods of diagnosis amongst which: clinical examination, sonar and mammography, which is the best and more effective in detecting breast cancer. Detection of breast tumors is difficult because of the weak illumination in the image and the overlap between regions. Segmentation is one the crucial steps in locating the tumors, which is an important method of diagnosis of the computer. In this study, segmentation techniques are proposed based on; classic morphology and fuzzy morphology, and a comparison between them. The proposed methods were tested using the database of mini -MIAS, which contains 322 images. After the comparison the statistical results, it shows, the detection of tumor boundary with fuzzy morphology give the higher accuracy than the results in classic morphology. The accuracy is 60.69%, 58.61% respectively due to the high flexibility of foggy logic in dealing with the low lighting in the medical images.

scholarly journals A Novel Computer-Aided-Diagnosis System for Breast Ultrasound Images Based on BI-RADS Categories

2020 ◽  
Vol 10 (5) ◽  
pp. 1830
Author(s):  
Yi-Wei Chang ◽  
Yun-Ru Chen ◽  
Chien-Chuan Ko ◽  
Wei-Yang Lin ◽  
Keng-Pei Lin
Keyword(s):  
Breast Tumor ◽  
Breast Imaging ◽  
Breast Lesion ◽  
Low Cost ◽  
Breast Ultrasound ◽  
Tissue Imaging ◽  
Support Vector ◽  
Ultrasound Images ◽  
Traditional Assessment ◽  
Cad System

The breast ultrasound is not only one of major devices for breast tissue imaging, but also one of important methods in breast tumor screening. It is non-radiative, non-invasive, harmless, simple, and low cost screening. The American College of Radiology (ACR) proposed the Breast Imaging Reporting and Data System (BI-RADS) to evaluate far more breast lesion severities compared to traditional diagnoses according to five-criterion categories of masses composition described as follows: shape, orientation, margin, echo pattern, and posterior features. However, there exist some problems, such as intensity differences and different resolutions in image acquisition among different types of ultrasound imaging modalities so that clinicians cannot always identify accurately the BI-RADS categories or disease severities. To this end, this article adopted three different brands of ultrasound scanners to fetch breast images for our experimental samples. The breast lesion was detected on the original image using preprocessing, image segmentation, etc. The breast tumor’s severity was evaluated on the features of the breast lesion via our proposed classifiers according to the BI-RADS standard rather than traditional assessment on the severity; i.e., merely using benign or malignant. In this work, we mainly focused on the BI-RADS categories 2–5 after the stage of segmentation as a result of the clinical practice. Moreover, several features related to lesion severities based on the selected BI-RADS categories were introduced into three machine learning classifiers, including a Support Vector Machine (SVM), Random Forest (RF), and Convolution Neural Network (CNN) combined with feature selection to develop a multi-class assessment of breast tumor severity based on BI-RADS. Experimental results show that the proposed CAD system based on BI-RADS can obtain the identification accuracies with SVM, RF, and CNN reaching 80.00%, 77.78%, and 85.42%, respectively. We also validated the performance and adaptability of the classification using different ultrasound scanners. Results also indicate that the evaluations of F-score based on CNN can obtain measures higher than 75% (i.e., prominent adaptability) when samples were tested on various BI-RADS categories.

scholarly journals Interleukin-6–mediated epigenetic control of the VEGFR2 gene induces disorganized angiogenesis in human breast tumors

2020 ◽  
Vol 295 (34) ◽  
pp. 12086-12098
Author(s):  
Mangala Hegde ◽  
Kanive Parashiva Guruprasad ◽  
Lingadakai Ramachandra ◽  
Kapaettu Satyamoorthy ◽  
Manjunath B. Joshi
Keyword(s):  
T Cells ◽  
Interleukin 6 ◽  
Breast Tumor ◽  
Dna Methyltransferase ◽  
Growth Factor Receptor ◽  
Breast Tumors ◽  
Dominant Negative ◽  
Human Breast ◽  
Paracrine Factors ◽  
Altered Expression

Disorganized vessels in the tumor vasculature lead to impaired perfusion, resulting in reduced accessibility to immune cells and chemotherapeutic drugs. In the breast tumor–stroma interplay, paracrine factors such as interleukin-6 (IL-6) often facilitate disordered angiogenesis. We show here that epigenetic mechanisms regulate the crosstalk between IL-6 and vascular endothelial growth factor receptor 2 (VEGFR2) signaling pathways in myoepithelial (CD10+) and endothelial (CD31+, CD105+, CD146+, and CD133−) cells isolated from malignant and nonmalignant tissues of clinically characterized human breast tumors. Tumor endothelial (Endo-T) cells in 3D cultures exhibited higher VEGFR2 expression levels, accelerated migration, invasion, and disorganized sprout formation in response to elevated IL-6 levels secreted by tumor myoepithelial (Epi-T) cells. Constitutively, compared with normal endothelial (Endo-N) cells, Endo-T cells differentially expressed DNA methyltransferase isoforms and had increased levels of IL-6 signaling intermediates such as IL-6R and signal transducer and activator of transcription 3 (STAT3). Upon IL-6 treatment, Endo-N and Endo-T cells displayed altered expression of the DNA methyltransferase 1 (DNMT1) isoform. Mechanistic studies revealed that IL-6 induced proteasomal degradation of DNMT1, but not of DNMT3A and DNMT3B and subsequently led to promoter hypomethylation and expression/activation of VEGFR2. IL-6–induced VEGFR2 up-regulation was inhibited by overexpression of DNMT1. Transfection of a dominant-negative STAT3 mutant, but not of STAT1, abrogated VEGFR2 expression. Our results indicate that in the breast tumor microenvironment, IL-6 secreted from myoepithelial cells influences DNMT1 stability, induces the expression of VEGFR2 in endothelial cells via a promoter methylation–dependent mechanism, and leads to disordered angiogenesis.

Epileptic Seizure Detection System Based on Multi-Domain Feature and Spike Feature of EEG

2019 ◽  
Vol 16 (04) ◽  
pp. 1950016 ◽  
Author(s):  
Duanpo Wu ◽  
Zimeng Wang ◽  
Hong Huang ◽  
Guangsheng Wang ◽  
Junbiao Liu ◽  
...  
Keyword(s):  
Detection System ◽  
Epileptic Seizures ◽  
Seizure Detection ◽  
Spike Rate ◽  
Linear Discriminant ◽  
Positive Ratio ◽  
False Positive Ratio ◽  
Eeg Data ◽  
The Family

Epilepsy is caused by sudden abnormal discharges of neurons in the brain. This paper constructs an automatic seizure detection system, which combines the predicting result of multi-domain feature with the predicting result of spike rate feature to detect the occurrence of epileptic seizures. After segmenting EEG data into 5[Formula: see text]s with 80% overlap epochs, the paper extracts time domain features, frequency domain features and hurst exponents (HE) from each epoch and these features are reduced by linear discriminant analysis (LDA) to be input parameters of the random forest (RF) classifier, which provides classification of the EEG epochs concerning the existence of seizures. In parallel, the paper extracts spikes from EEG data with morphological filter and calculates the spike rate to determine whether there is seizure. Then the results obtained by these two methods are merged as the final detection result. The paper shows that the accuracy (AC), sensitivity (SE), specificity (SP) and the false positive ratio based on event (FPRE) obtained by hybrid method are 98.94%, 76.60%, 98.99% and 2.43 times/h, respectively. Finally, the paper applies the seizure detection method to do seizure warning and recording to help the family member to take care of the patients and the doctor to adjust the antiepileptic drugs (AEDs).

The enhanced chemotherapeutic effects of doxorubicin loaded PEG coated TiO2 nanocarriers in an orthotopic breast tumor bearing mouse model

2015 ◽  
Vol 3 (8) ◽  
pp. 1518-1528 ◽  
Author(s):  
Yang Du ◽  
Wenzhi Ren ◽  
Yaqian Li ◽  
Qian Zhang ◽  
Leyong Zeng ◽  
...  
Keyword(s):  
Side Effects ◽  
Mouse Model ◽  
Breast Tumor ◽  
Breast Tumors ◽  
Tumor Bearing Mouse ◽  
Tumor Bearing

TiO2–PEG–DOX nanoparticles improve the chemotherapeutic effects of doxorubicin in orthotopic breast tumors and minimize the DOX side effects.

Diagnostic Values of Breast Imaging Reporting and Data System and ultrasound Elastography in Benign and Malignant Breast Tumor

2016 ◽  
Vol 13 (10) ◽  
pp. 6509-6513
Author(s):  
Xin-Hua Lu
Keyword(s):  
Breast Cancer ◽  
Roc Curve ◽  
Predictive Value ◽  
Breast Tumor ◽  
Breast Imaging ◽  
Data System ◽  
Ultrasound Elastography ◽  
Malignant Breast Tumor ◽  
Diagnostic Values ◽  
Malignant Breast

Objective: To evaluate the diagnostic values of Breast Imaging Reporting and Data System (BI-RADS), ultrasound elastography (UE) and the combination in differentiating benign and malignant breast tumor. Methods: The BI-RADS and UE image features of 248 breast cancer patients (a total of 260 lesions) proved by surgery and pathology from February 2013 to March 2015 were retrospectively analyzed. With the pathologic results as the gold standard, the sensitivity, specificity, positive and negative predictive values, and accuracy were calculated for BI-RADS, UE and the combination. On the basis of the sensitivity and specificity, they were analyzed by receiver operating characteristic (ROC) curve. Results: In all 260 lesions, 71 lesions were benign and 189 were malignant according to UE diagnosis; 50 lesions were benign and 210 were malignant proved by BI-RADS; 55 lesions were benign and 205 were malignant diagnosed by the combination. The sensitivity (86.09%), specificity (61.64%), positive predictive value (85.19%), negative predictive value (63.38%), and accuracy (79.23%) of ultrasound elastography were all less than that of BI-RADS (98.39%, 64.38%, 88.85%, 87.62%, 94.00%) and the combination (99.47%, 73.97%, 92.31%, 90.73%, 98.18%). The areas under the ROC curve for UE, BI-RADS and the combination were respectively 0.746[95%CI(0.673–0.818)], 0.814[95%CI(0.744–0.884)] and 0.867[95%CI(0.805–0.929)]. Conclusion: Ultrasonic BI-RADS can be the first choice for diagnosing breast cancer, with UE as the auxiliary method. The combined application can further improve the diagnosis rate of benign and malignant breast tumor.

Abstract Concept Instantiation with Context Relevance Measurement

2020 ◽  
Author(s):  
Shengwei Gu ◽  
Xiangfeng Luo ◽  
Hao Wang ◽  
Jing Huang ◽  
Subin Huang
Keyword(s):  
Web Search ◽  
False Negative ◽  
Model Performance ◽  
Category Structure ◽  
Abstract Concept ◽  
Contextual Constraint ◽  
Proposed Model ◽  
The Right ◽  
Novel Model ◽  
Walk Algorithm

In different contexts, one abstract concept (e.g., fruit) may be mapped into different concrete instance sets, which is called abstract concept instantiation. It has been widely applied in many applications, such as web search, intelligent recommendation, etc. However, in most abstract concept instantiation models have the following problems: (1) the neglect of incorrect label and label incompleteness in the category structure on which instance selection relies; (2) the subjective design of instance profile for calculating the relevance between instance and contextual constraint. The above problems lead to false prediction in terms of abstract concept instantiation. To tackle these problems, we proposed a novel model to instantiate the abstract concept. Firstly, to alleviate the incorrect label and remedy label incompleteness in the category structure, an improved random-walk algorithm is proposed, called InstanceRank, which not only utilize the category information, but it also exploits the association information to infer the right instances of an abstract concept. Secondly, for better measuring the relevance between instances and contextual constraint, we learn the proper instance profile from different granularity ones. They are designed based on the surrounding text of the instance. Finally, noise reduction and instance filtering are introduced to further enhance the model performance. Experiments on Chinese food abstract concept set show that the proposed model can effectively reduce false positive and false negative of instantiation results.

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