scholarly journals Using parallel pre-trained types of DCNN model to predict breast cancer with color normalization

2022 ◽  
Vol 15 (1) ◽  
Author(s):  
William Al Noumah ◽  
Assef Jafar ◽  
Kadan Al Joumaa
Keyword(s):  
Breast Cancer ◽  
Expert System ◽  
Early Diagnosis ◽  
Suggested Model ◽  
Proposed Model ◽  
Joint Experience ◽  
Better Than

Abstract Objective Breast cancer is the most common among women, and it causes many deaths every year. Early diagnosis increases the chance of cure through treatment. The traditional manual diagnosis requires effort and time from pathological experts, as it needs a joint experience of a number of pathologists. Diagnostic mistakes can lead to catastrophic results and endanger the lives of patients. The presence of an expert system that is able to specify whether the examined tissue is healthy or not, thus improves the quality of diagnosis and saves the time of experts. In this paper, a model capable of classifying breast cancer anatomy by making use of a pre-trained DCNN has been proposed. To build this model, first of all the image should be color stained by using Vahadane algorithm, then the model which combines three pre-trained DCNN (Xception, NASNet and Inceptoin_Resnet_V2) should be built in parallel, then the three branches should be aggregated to take advantage of each other. The suggested model was tested under different values of threshold ratios and also compared with other models. Results The proposed model on the BreaKHis dataset achieved 98% accuracy, which is better than the accuracy of other models used in this field.

scholarly journals NEW INFORMATION TECHNOLOGIES FOR ANALYSIS SKELETON PLANAR SCINTIGRAMMS OF PATIENTS WITH BREAST CANCER

2019 ◽  
Vol 23 (2) ◽  
pp. 187-196
Author(s):  
Xinmei Kang ◽  
N. E. Kosykh ◽  
E. A. Levkova ◽  
V. A. Razuvaev ◽  
S. Z. Savin
Keyword(s):  
Breast Cancer ◽  
Expert System ◽  
Bone Metastases ◽  
Information Technologies ◽  
Metastatic Lesion ◽  
High Tech ◽  
Mathematical Methods ◽  
System Building ◽  
Average Brightness

In work is described practical approach to the expert system building for the analysis skeleton planar scintigramms. The aim is to analyze the numerical characteristics of bone metastases by scintigraphy. Objective. Progress in the development of bioinformatics and mathematical methods in biomedicine, as well as the development of computer and telecommunications systems and networks determines the look of the present and future of oncology technology and of medicine in general. At last years of one of the directions of high-tech-medicine development is a processing the digital image: improvement of quality of image, recovering image, its recognition of separate elements. Recognition of pathological processes is one of the most important problems of processing the medical image. Methods and results. Method of computer-aided analysis of planar osteostsintigrammy studied the skeleton of patients with breast cancer are in complete remission and in the phase progression of the disease with metastases to the skeleton. As analyzed parameter was used brightness of images. The study of the physiological accumulation of radiopharmaceuticals in patients without metastasis to the skeleton indicates a wide variation in the brightness values of the scintigram in some areas of the skeleton. At the same anatomical areas of the skeleton there are significant differences in the values of the index of average brightness. In almost all areas of the skeleton averages of the brightness lesions hyperfixation RFP for scintigram significantly prevail over those of «physiological» lesions hyperfixation. Thus, there is a direct relationship between the levels of accumulation of the radiopharmaceutical in areas of the skeleton without metastatic lesion and bone metastases occurring in these zones. Consider methodological approaches to studies of quality of qualifier at the expert system building for the analysis skeleton planar scintigramms, as well as results of conducting calculations.

scholarly journals Cloud-Based Breast Cancer Prediction Empowered with Soft Computing Approaches

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Farrukh Khan ◽  
Muhammad Adnan Khan ◽  
Sagheer Abbas ◽  
Atifa Athar ◽  
Shahan Yamin Siddiqui ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Expert System ◽  
Soft Computing ◽  
Early Stage ◽  
Health Sector ◽  
Cancer Prediction ◽  
Detection Model ◽  
Initial Stage ◽  
Precision Rate ◽  
Better Than

The developing countries are still starving for the betterment of health sector. The disease commonly found among the women is breast cancer, and past researches have proven results that if the cancer is detected at a very early stage, the chances to overcome the disease are higher than the disease treated or detected at a later stage. This article proposed cloud-based intelligent BCP-T1F-SVM with 2 variations/models like BCP-T1F and BCP-SVM. The proposed BCP-T1F-SVM system has employed two main soft computing algorithms. The proposed BCP-T1F-SVM expert system specifically defines the stage and the type of cancer a person is suffering from. Expert system will elaborate the grievous stages of the cancer, to which extent a patient has suffered. The proposed BCP-SVM gives the higher precision of the proposed breast cancer detection model. In the limelight of breast cancer, the proposed BCP-T1F-SVM expert system gives out the higher precision rate. The proposed BCP-T1F expert system is being employed in the diagnosis of breast cancer at an initial stage. Taking different stages of cancer into account, breast cancer is being dealt by BCP-T1F expert system. The calculations and the evaluation done in this research have revealed that BCP-SVM is better than BCP-T1F. The BCP-T1F concludes out the 96.56 percentage accuracy, whereas the BCP-SVM gives accuracy of 97.06 percentage. The above unleashed research is wrapped up with the conclusion that BCP-SVM is better than the BCP-T1F. The opinions have been recommended by the medical expertise of Sheikh Zayed Hospital Lahore, Pakistan, and Cavan General Hospital, Lisdaran, Cavan, Ireland.

scholarly journals Hybrid Machine Learning Model of Extreme Learning Machine Radial Basis Function for Breast Cancer Detection and Diagnosis: A Multilayer Fuzzy Expert System

2019 ◽  
Author(s):  
Sanaz Mojrian ◽  
Gergo Pinter ◽  
Javad Hassannataj Joloudari ◽  
Imre Felde ◽  
Narjes Nabipour ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Expert System ◽  
Radial Basis Function ◽  
Basis Function ◽  
Fuzzy Expert System ◽  
Proposed Model ◽  
Svm Model ◽  
Learning Machine ◽  
Linear Svm

Mammography is often used as the most common laboratory method for the detection of breast cancer, yet associated with the high cost and many side effects. Machine learning prediction as an alternative method has shown promising results. This paper presents a method based on a multilayer fuzzy expert system for the detection of breast cancer using an extreme learning machine (ELM) classification model integrated with radial basis function (RBF) kernel called ELM-RBF, considering the Wisconsin dataset. The performance of the proposed model is further compared with a linear-SVM model. The proposed model outperforms the linear-SVM model with RMSE, R2, MAPE equal to 0.1719, 0.9374 and 0.0539, respectively. Furthermore, both models are studied in terms of criteria of accuracy, precision, sensitivity, specificity, validation, true positive rate (TPR), and false-negative rate (FNR). The ELM-RBF model for these criteria presents better performance compared to the SVM model.

scholarly journals Analysis of the Risk Factors of Upper Limb Lymphedema after Breast Cancer Surgery and Nursing Care

2021 ◽  
Vol 5 (5) ◽  
pp. 12-17
Author(s):  
Linlin Qian
Keyword(s):  
Breast Cancer ◽  
Quality Of Life ◽  
Upper Limb ◽  
Nursing Intervention ◽  
Cancer Surgery ◽  
Breast Cancer Surgery ◽  
Delayed Healing ◽  
Functional Exercise ◽  
Better Than

Objective: To evaluate and analyze the influencing factors of upper limb lymphedema after breast cancer surgery, and to study effective nursing intervention measures. Methods: 500 cases of early breast cancer patients from October 2017 to December 2020 were selected, all patients underwent surgical intervention, retrospectively analyzed the basic clinical data of patients, and statistically analyzed the influencing factors of upper limb lymphedema. All patients with upper extremity lymphedema received high-quality nursing intervention, and the specific nursing effect was analyzed. Results: Logistic regression analysis showed that the risk factors of upper limb lymphedema after breast cancer surgery included hypertension, postoperative upper limb functional exercise, delayed healing of incision, radiotherapy and so on. After nursing intervention, the patients’ elbow 10cm, elbow 10cm, wrist size value and VAS score were better than those before nursing (P < 0.05). The quality of life score of patients after nursing intervention was significantly better than that before nursing (P < 0.05). Conclusion: Hypertension, postoperative upper limb functional exercise, delayed healing of incision, radiotherapy and other factors can induce upper limb lymphedema after breast cancer surgery. Effective nursing intervention can alleviate the condition of patients with upper limb lymphedema and improve their quality of life, which is worthy of comprehensive promotion.

scholarly journals A new paradigm of screening and early diagnosis: assessment of benefits and harms

2020 ◽  
Vol 66 (6) ◽  
pp. 589-602
Author(s):  
Давид Заридзе ◽  
Dmitry Maksimovich ◽  
Ivan Stilidi
Keyword(s):  
Breast Cancer ◽  
Quality Of Life ◽  
Prostate Cancer ◽  
Clinical Trials ◽  
Early Diagnosis ◽  
The United States ◽  
Diagnostic Methods ◽  
New Paradigm

Abstract The article presents scientific evidence that confirms the new paradigm that  “early” diagnosis is not always beneficial, and that screening and early diagnosis can do more harm than good. As a result, of screening, in a number of cases, lesions are diagnosed that, although have histological patterns of cancer, are often clinically insignificant, indolent i.e. overdiagnosis takes place. Such lesions primarily include latent cancers of the prostate and thyroid gland. An increase in the incidence of certain types of cancers in the United States and other developed countries, as a result, of the introduction of PSA screening, mammography, ultrasound examination of the neck and other highly sensitive diagnostic methods, with stable or decreasing mortality, is a sign of overdiagnosis. In Russia, there is also a marked increase in the incidence of cancer of the prostate, breast, thyroid, kidney and melanoma, while mortality from these forms of cancer is stable or decreasing. The increase in the incidence of all malignant formations in Russian, as in American men, is determined by the increase in the incidence of prostate cancer. In randomized clinical trials of the efficacy of screening for prostate and breast cancer, an excess of the detected cases of cancer in the screening group compared with the control group indicates overdiagnosis. With an increase in follow-up (10-15 years), the number of excess cases in the screening group decreases. However, in some studies even after 10-15 years of follow-up, the excess of cancer cases in the screening group persisted, i.e. overdiagnosis was confirmed. Thus, the problem of overdiagnosis is also relevant to controlled clinical trials, despite a well-verified protocol and strict adherence to it. The danger of overdiagnosis in real life, daily practice, and especially with opportunistic screening, which, by definition, is carried out without quality control, is much higher. Overdiagnosis often leads to unnecessary, sometimes excessive treatment and a deterioration in the quality of life of patients who are not cancer patients. Refusal of aggressive therapy and active follow-up should be the method of choice for the management of patients with asymptomatic neoplasms identified at the screening. Such tactics will avoid unnecessary and excessive interventions, which, in turn, will prevent a deterioration in the quality of life of patients and, in addition, will reduce the cost of treatment. Key words: overdiagnosis, screening, early diagnosis, trends in incidence and mortality, prostate cancer, breast cancer, thyroid cancer

scholarly journals Dilated Convolution with Dilated GRU for Music Source Separation

2019 ◽  
Author(s):  
Jen-Yu Liu ◽  
Yi-Hsuan Yang
Keyword(s):  
High Resolution ◽  
Source Separation ◽  
Receptive Fields ◽  
Audio Signals ◽  
Sound Sources ◽  
Dilated Convolution ◽  
Proposed Model ◽  
Music Audio ◽  
Better Than

Stacked dilated convolutions used in Wavenet have been shown effective for generating high-quality audios. By replacing pooling/striding with dilation in convolution layers, they can preserve high-resolution information and still reach distant locations. Producing high-resolution predictions is also crucial in music source separation, whose goal is to separate different sound sources while maintain the quality of the separated sounds. Therefore, in this paper, we use stacked dilated convolutions as the backbone for music source separation. Although stacked dilated convolutions can reach wider context than standard convolutions do, their effective receptive fields are still fixed and might not be wide enough for complex music audio signals. To reach even further information at remote locations, we propose to combine a dilated convolution with a modified GRU called Dilated GRU to form a block. A Dilated GRU receives information from k-step before instead of the previous step for a fixed k. This modification allows a GRU unit to reach a location with fewer recurrent steps and run faster because it can execute in parallel partially. We show that the proposed model with a stack of such blocks performs equally well or better than the state-of-the-art for separating both vocals and accompaniment.

scholarly journals Hybrid Machine Learning Model of Extreme Learning Machine Radial Basis Function for Breast Cancer Detection and Diagnosis: A Multilayer Fuzzy Expert System

2020 ◽  
Author(s):  
Sanaz Mojrian ◽  
Gergo Pinter ◽  
Javad Hassannataj Joloudari ◽  
Imre Felde ◽  
Akos Szabo-Gali ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Expert System ◽  
Radial Basis Function ◽  
Basis Function ◽  
Fuzzy Expert System ◽  
Proposed Model ◽  
Svm Model ◽  
Learning Machine ◽  
Linear Svm

Mammography is often used as the most common laboratory method for the detection of breast cancer, yet associated with the high cost and many side effects. Machine learning prediction as an alternative method has shown promising results. This paper presents a method based on a multilayer fuzzy expert system for the detection of breast cancer using an extreme learning machine (ELM) classification model integrated with radial basis function (RBF) kernel called ELM-RBF, considering the Wisconsin dataset. The performance of the proposed model is further compared with a linear-SVM model. The proposed model outperforms the linear-SVM model with RMSE, R2, MAPE equal to 0.1719, 0.9374 and 0.0539, respectively. Furthermore, both models are studied in terms of criteria of accuracy, precision, sensitivity, specificity, validation, true positive rate (TPR), and false-negative rate (FNR). The ELM-RBF model for these criteria presents better performance compared to the SVM model.

scholarly journals Hybrid Machine Learning Model of Extreme Learning Machine Radial basis function for Breast Cancer Detection and Diagnosis; a Multilayer Fuzzy Expert System

2020 ◽  
Author(s):  
Sanaz Mojrian ◽  
Gergo Pinter ◽  
Javad Hassannataj Joloudari ◽  
Imre Felde ◽  
Akos Szabo-Gali ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Expert System ◽  
Radial Basis Function ◽  
Basis Function ◽  
Fuzzy Expert System ◽  
Proposed Model ◽  
Svm Model ◽  
Learning Machine ◽  
Linear Svm

AbstractMammography is often used as the most common laboratory method for the detection of breast cancer, yet associated with the high cost and many side effects. Machine learning prediction as an alternative method has shown promising results. This paper presents a method based on a multilayer fuzzy expert system for the detection of breast cancer using an extreme learning machine (ELM) classification model integrated with radial basis function (RBF) kernel called ELM-RBF, considering the Wisconsin dataset. The performance of the proposed model is further compared with a linear-SVM model. The proposed model outperforms the linear-SVM model with RMSE, R2, MAPE equal to 0.1719, 0.9374 and 0.0539, respectively. Furthermore, both models are studied in terms of criteria of accuracy, precision, sensitivity, specificity, validation, true positive rate (TPR), and false-negative rate (FNR). The ELM-RBF model for these criteria presents better performance compared to the SVM model.

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