A Computer Aided Diagnosis System in Mammography Using Artificial Neural Networks

Purpose In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing. Design/methodology/approach In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing. Findings In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing. Originality/value In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

To investigate the value of computer-aided diagnosis (CAD) system in assessing thyroid nodules concurrent with Hashimoto's thyroiditis (HT). Totally 148 patients with 193 thyroid nodules were enrolled. A radiologist assessed the nodules using a thyroid ultrasound CAD system. Additionally, the nodules were evaluated by one experienced radiologist alone, and one training radiologist without and with CAD assistance. The diagnostic performance was compared between the CAD system and the experienced radiologist, and the training radiologist without and with CAD assistance. The CAD system demonstrated a similar sensitivity to that of the experienced radiologist in diagnosing thyroid cancers (89.8% versus 92.4%, P > .05). The specificity and accuracy of the CAD system were lower than that of the experienced radiologist in assessing the nodules with diffusedly altered glands (specificity, 60.0% versus 81.7%, P=.007; accuracy, 77.5% versus 88.1%, P=.011). With CAD assistance, the training radiologist had improved sensitivity and accuracy that increased to 87.9% and 86.8% in classifying nodules with sonographically evident HT (both P=.012). The CAD system has comparable sensitivity, but lower specificity compared with the experienced radiologist in diagnosing thyroid malignancies concurrent with HT. For a radiologist with less experience, the CAD system can help improve the diagnostic performance by increasing sensitivity and accuracy in assessing thyroid nodules with diffusely altered parenchyma.

Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system

The manual examination of histological images like computed tomography (CT) images by physicians is prone to subjectivity and limited intra and inter-surgeon reproducibility, due to its heavy reliance on human interpretation. As result of which, diagnosis of cancer especially in lungs becomes less accurate and unreliable. So, a computer-aided diagnosis (CAD) system, based on artificial intelligence that efficiently detects nodules of any shape and size, is used for diagnosis without human intervention. In this work, we have developed a two stage CAD system in which the first stage involves pre-processing applied for a better quality image to enable higher success rate on detection following which the cancerous nodule region is segmented. The second stage involves artificial neural network (ANN) architecture which is trained using a modified BFGS algorithm. The proposed system was trained, tested, and evaluated specifically on the problem of detecting lung cancer nodules found on CT images to give a positive detection. A significant comparative analysis was done between the proposed method and several existing CAD systems used for lung nodule diagnosis and the proposed method using training-based neural networks prove to provide accuracy of 96.7% and also better specificity; thus, the overall performance of the CAD scheme was improved substantially.

This paper presents a computer-aided diagnosis (CAD) system for automatic detection of clustered microcalcifications (MCs) in digitized mammograms. The proposed system consists of two main steps. First, potential MC pixels in the mammograms are segmented out by using four mixed features consisting of two wavelet features and two gray level statistical features and then the potential MC pixels are labeled into potential individual MC objects by their spatial connectivity. Second, MCs are detected by extracting a set of 17 features from the potential individual MC objects. The classifier which is used in the first step is a multilayer feedforward neural network classifier but for the second step we have used Adaboost with SVM-based component classifier. Component classifiers which we used in our combining method are SVM classifiers with RBF kernel. The method was applied to a database of 40 mammograms (Nijmegen database) containing 105 clusters of MCs. A free-response operating characteristics (FROC) curve is used to evaluate the performance of CAD system. Results show that the proposed system gives quite satisfactory performance. In particular, 89.55% mean true positive detection rate is achieved at the cost of 0.921 false positive per image.

Physical education at college is the final step of school physical education, and it is critical to a student's educational experience. There are numerous issues such as the integration of existing teaching resources, combining the characteristics of basketball teaching to improve teaching quality, and the use of basketball as a means of lifelong physical exercise faced by college basketball teachers. To address these challenges, this research attempts to integrate the backpropagation (BP) neural network technique with its enhanced deep learning algorithm by adjusting the learning rate, adding more momentum items, increasing the excitation function, and other methods. A teacher’s effectiveness as a teacher is examined using numerous variables utilizing the suggested BP neural network assessment model for measuring teaching quality. In addition, the specific implementation of the improved BP neural network algorithm includes the determination of the number of BP neural network layers, the input data preparation method, and the number of neurons per layer. Finally, the simulation experiment evaluates the teacher’s teaching quality using the upgraded BP neural network.

Due to the latest developments in communication and computing, smart services and applications are being deployed for various applications such as entertainment, health care, smart homes, security and surveillance. In intelligent communication environments, the main difficulty arising in designing an efficient congestion control scheme lies in the large propagation delay in data transfer which usually leads to a mismatch between the network resources and the amount of admitted traffic. To attack this problem, this paper describes a novel congestion control scheme in intelligent communication environments, which is based on a Back Propagation (BP) neural network technique. We consider a general computer communication model with multiple sources and one destination node. The dynamic buffer occupancy of the bottleneck node is predicted and controlled by using a BP neural network. The controlled best-effort traffic of the sources uses the bandwidth, which is left over by the guaranteed traffic. This control mechanism is shown to be able to avoid network congestion efficiently and to optimize the transfer performance both by the theoretic analyzing procedures and by the simulation studies.

Icing condition prediction of wind turbine blade by using artificial neural network based on modal frequency

Computer-Aided Diagnosis (CAD) systems provide a second opinion to health professionals about the possible existence of an anomaly. Evaluation of CAD systems is a challenge and most of the traditional metrics requires the constant participation of experts. This paper presents an approach for evaluating CAD systems using concepts of Content-Based Image Retrieval and graphical oracles. After implementing feature descriptors and selecting three similarity functions, two metrics are proposed to measure the efficiency of CAD systems. A case study was conducted considering three simulated CAD systems to detect masses and calcifications in mammographic images. The results indicated that the our approach is as robust as traditional metrics with respect to performance evaluation. However, our approach is more flexible than traditional metrics because evaluators can choose the more adequate features to assess a particular CAD system.

This paper presents a novel feature extraction method using the combination of the Coordinate Logic Filters (CLF) and Artificial Neural Networks (ANN) applied to 2D signals (Images). The method consists of image enhancement by histogram adaptive equalization technique, features extraction by modifying gray levels applying a nonlinear adaptive transformation function and edge detection by Coordinate Logic Filters (CLF), generation, clustering and labelling of suboptimal features vectors by Self Organizing Map (SOM) Neural Network. For the detection we applied Back Propagation Neural Network (BPNN). This method is tested to detect Microcalcifications (MCs) in Regions of Interest (ROI) from mammograms. The experiment results show that the proposed method can locate MCs in an efficient way, moreover the method promise interesting advances in Medical Industry.

Computer-aided diagnosis (CAD) systems in mammography have been developed and investigated for several years and recently they have left the research stage to enter the clinical stage. The purpose of this article is to review the present situation of mammography for breast cancer detection and the role played by CAD systems. Results from the recent literature show that CAD systems have the potential to improve the sensitivities of radiologists in the detection of malignant clustered microcalcifications and masses, while keeping specificities at acceptable levels. This leads to the conclusion that CAD systems can be incorporated into clinical practice as a double reading option to radiologists. However, some issues have yet to be tackled for CAD systems to gain better acceptance and more widespread use worldwide.

Computer-aided diagnosis of peripheral soft tissue masses based on ultrasound imaging

ObjectiveWe wanted to investigate the usefulness of a computer-aided diagnosis (CAD) system in assisting radiologists to diagnosis malignant solitary pulmonary nodules (SPNs), as compared with diagnosing SPNs with using direct personal drawing.Materials and MethodsForty patients with SPNs were analyzed. After the pre-contrast scan was performed, an additional ten series of post-contrast images were obtained at 20-second intervals. Two investigators measured the attenuation values of the SPNs: a radiologist who drew the regions of interest (ROIs), and a technician who used a CAD system. The Bland and Altman plots were used to compare the net enhancement between a CAD system and direct personal drawing. The diagnostic characteristics of the malignant SPNs were calculated by considering the CAD and direct personal drawing and with using Fisher's exact test.ResultsOn the Bland and Altman plot, the net enhancement difference between the CAD system and direct personal drawing was not significant (within ± 2 standard deriation). The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of diagnosing malignant SPNs using CAD was 92%, 85%, 75%, 96% and 88%, respectively. The sensitivity, specificity, PPV, NPV and accuracy of diagnosing malignant SPNs using direct drawing was 92%, 89%, 79%, 92% and 88%, respectively.ConclusionThe CAD system was a useful tool for diagnosing malignant SPNs.

Bone metastasis has a significant influence on the prognosis of prostate cancer(PCa) patients. In this review, we discussed the current application of PCa bone metastasis diagnosis with single-photon emission computed tomography (SPECT) and positron emission tomography/computed tomography (PET/CT) computer-aided diagnosis(CAD) systems. A literature search identified articles concentrated on PCa bone metastasis and PET/CT or SPECT CAD systems using the PubMed database. We summarized the previous studies focused on CAD systems and manual quantitative markers calculation, and the coincidence rate was acceptable. We also analyzed the quantification methods, advantages, and disadvantages of CAD systems. CAD systems can detect abnormal lesions of PCa patients’ 99mTc-MDP-SPECT, 18F-FDG-PET/CT, 18F-NaF-PET/CT, and 68 Ga-PSMA PET/CT images automated or semi-automated. CAD systems can also calculate the quantitative markers, which can quantify PCa patients’ whole-body bone metastasis tumor burden accurately and quickly and give a standardized and objective result. SPECT and PET/CT CAD systems are potential tools to monitor and quantify bone metastasis lesions of PCa patients simply and accurately, the future clinical application of CAD systems in diagnosing PCa bone metastasis lesions is necessary and feasible.

BackgroundThe diagnosis of thyroid cancer and distinguishing benign from malignant thyroid nodules by junior radiologists can be challenging. This study aimed to develop a computer-aided diagnosis (CAD) system based on the Thyroid Imaging Reporting and Data System (TI-RADS) to distinguish benign from malignant thyroid nodules by analyzing ultrasound images to improve the diagnostic performance of junior radiologists.Material/MethodsA modified TI-RADS based on a convolutional neural network (CNN) was used to develop the CAD system. This retrospective study reviewed 789 thyroid nodules from 695 patients and included radiologists with different diagnostic experience. Five study groups included the CAD group, the junior radiologist group, the intermediate-level radiologist group, the senior radiologist group, and the group in which the junior radiologist used the CAD system. The ultrasound findings were reviewed and compared with the histopathology diagnosis.ResultsThe CAD system for the diagnosis of thyroid cancer showed an accuracy of 80.35%, a sensitivity of 80.64%, a specificity of 80.13%, a positive predictive value (PPV) of 76.02%, a negative predictive value (NPV) of 84.12%, and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.87. The accuracy of the junior radiologists in diagnosing thyroid cancer using CAD was similar to that of intermediate-level radiologists (79.21% vs. 77.57%; P=0.427).ConclusionsThe use of ultrasound CAD based on the TI-RADS showed potential for distinguishing between benign and malignant thyroid nodules and improved the diagnostic performance of junior radiologists.