Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
Early detection of breast cancer using mammography images and software engine...TELKOMNIKA JOURNAL
The breast cancer has affected a wide region of women as a particular case. Therefore, different researchers have focused on the early detection of this disease to overcome it in efficient way. In this paper, an early breast cancer detection system has been proposed based on mammography images. The proposed system adopts deep-learning technique to increase the accuracy of detection. The convolutional neural network (CNN) model is considered for preparing the datasets of training and test. It is important to note that the software engineering process model has been adopted in constructing the proposed algorithm. This is to increase the reliably, flexibility and extendibility of the system. The user interfaces of the system are designed as a website used at country side general purpose (GP) health centers for early detection to the disease under lacking in specialist medical staff. The obtained results show the efficiency of the proposed system in terms of accuracy up to more than 90% and decrease the efforts of medical staff as well as helping the patients. As a conclusion, the proposed system can help patients by early detecting the breast cancer at far places from hospital and referring them to nearest specialist center.
Applying Deep Learning to Transform Breast Cancer DiagnosisCognizant
Deep convolutional neural networks can assist pathologists in breast cancer diagnosis by automatically filtering benign tissue biopsies, identifying malignant regions and labeling important cellular features like nuclei for further analysis. Automatic detection of diagnostically relevant regions-of-interest and nuclei segmentation reduces the pathologist’s workload, while ensuring that no critical region is overlooked, rendering breast cancer diagnosis more reliable, efficient and cost-effective.
A Review of Super Resolution and Tumor Detection Techniques in Medical Imagingijtsrd
Images with high resolution are desirable in many applications such as medical imaging, video surveillance, astronomy etc. In medical imaging, images are obtained for medical investigative purposes and for providing information about the anatomy, the physiologic and metabolic activities of the volume below the skin. Medical imaging is an important diagnosis instrument to determine the presence of certain diseases. Therefore increasing the image resolution should significantly improve the diagnosis ability for corrective treatment. Brain tumor detection is used for identifying the tumor present in the Brain. MRI images help the doctors for identifying the Brain tumor size and shape of the tumor. The purpose of this report to provide a survey of research related super resolution and tumor detection methods. Fathimath Safana C. K | Sherin Mary Kuriakose ""A Review of Super Resolution and Tumor Detection Techniques in Medical Imaging"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23525.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/23525/a-review-of-super-resolution-and-tumor-detection-techniques-in-medical-imaging/fathimath-safana-c-k
BREAST CANCER DIAGNOSIS USING MACHINE LEARNING ALGORITHMS –A SURVEYijdpsjournal
Breast cancer has become a common factor now-a-days. Despite the fact, not all general hospitals
have the facilities to diagnose breast cancer through mammograms. Waiting for diagnosing a breast
cancer for a long time may increase the possibility of the cancer spreading. Therefore a computerized
breast cancer diagnosis has been developed to reduce the time taken to diagnose the breast cancer and
reduce the death rate. This paper summarizes the survey on breast cancer diagnosis using various machine
learning algorithms and methods, which are used to improve the accuracy of predicting cancer. This survey
can also help us to know about number of papers that are implemented to diagnose the breast cancer.
Breast cancer diagnosis via data mining performance analysis of seven differe...cseij
According to World Health Organization (WHO), breast cancer is the top cancer in women both in the
developed and the developing world. Increased life expectancy, urbanization and adoption of western
lifestyles trigger the occurrence of breast cancer in the developing world. Most cancer events are
diagnosed in the late phases of the illness and so, early detection in order to improve breast cancer
outcome and survival is very crucial.
In this study, it is intended to contribute to the early diagnosis of breast cancer. An analysis on breast
cancer diagnoses for the patients is given. For the purpose, first of all, data about the patients whose
cancers’ have already been diagnosed is gathered and they are arranged, and then whether the other
patients are in trouble with breast cancer is tried to be predicted under cover of those data. Predictions of
the other patients are realized through seven different algorithms and the accuracies of those have been
given. The data about the patients have been taken from UCI Machine Learning Repository thanks to Dr.
William H. Wolberg from the University of Wisconsin Hospitals, Madison. During the prediction process,
RapidMiner 5.0 data mining tool is used to apply data mining with the desired algorithms.
Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
Early detection of breast cancer using mammography images and software engine...TELKOMNIKA JOURNAL
The breast cancer has affected a wide region of women as a particular case. Therefore, different researchers have focused on the early detection of this disease to overcome it in efficient way. In this paper, an early breast cancer detection system has been proposed based on mammography images. The proposed system adopts deep-learning technique to increase the accuracy of detection. The convolutional neural network (CNN) model is considered for preparing the datasets of training and test. It is important to note that the software engineering process model has been adopted in constructing the proposed algorithm. This is to increase the reliably, flexibility and extendibility of the system. The user interfaces of the system are designed as a website used at country side general purpose (GP) health centers for early detection to the disease under lacking in specialist medical staff. The obtained results show the efficiency of the proposed system in terms of accuracy up to more than 90% and decrease the efforts of medical staff as well as helping the patients. As a conclusion, the proposed system can help patients by early detecting the breast cancer at far places from hospital and referring them to nearest specialist center.
Applying Deep Learning to Transform Breast Cancer DiagnosisCognizant
Deep convolutional neural networks can assist pathologists in breast cancer diagnosis by automatically filtering benign tissue biopsies, identifying malignant regions and labeling important cellular features like nuclei for further analysis. Automatic detection of diagnostically relevant regions-of-interest and nuclei segmentation reduces the pathologist’s workload, while ensuring that no critical region is overlooked, rendering breast cancer diagnosis more reliable, efficient and cost-effective.
A Review of Super Resolution and Tumor Detection Techniques in Medical Imagingijtsrd
Images with high resolution are desirable in many applications such as medical imaging, video surveillance, astronomy etc. In medical imaging, images are obtained for medical investigative purposes and for providing information about the anatomy, the physiologic and metabolic activities of the volume below the skin. Medical imaging is an important diagnosis instrument to determine the presence of certain diseases. Therefore increasing the image resolution should significantly improve the diagnosis ability for corrective treatment. Brain tumor detection is used for identifying the tumor present in the Brain. MRI images help the doctors for identifying the Brain tumor size and shape of the tumor. The purpose of this report to provide a survey of research related super resolution and tumor detection methods. Fathimath Safana C. K | Sherin Mary Kuriakose ""A Review of Super Resolution and Tumor Detection Techniques in Medical Imaging"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23525.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/23525/a-review-of-super-resolution-and-tumor-detection-techniques-in-medical-imaging/fathimath-safana-c-k
BREAST CANCER DIAGNOSIS USING MACHINE LEARNING ALGORITHMS –A SURVEYijdpsjournal
Breast cancer has become a common factor now-a-days. Despite the fact, not all general hospitals
have the facilities to diagnose breast cancer through mammograms. Waiting for diagnosing a breast
cancer for a long time may increase the possibility of the cancer spreading. Therefore a computerized
breast cancer diagnosis has been developed to reduce the time taken to diagnose the breast cancer and
reduce the death rate. This paper summarizes the survey on breast cancer diagnosis using various machine
learning algorithms and methods, which are used to improve the accuracy of predicting cancer. This survey
can also help us to know about number of papers that are implemented to diagnose the breast cancer.
Breast cancer diagnosis via data mining performance analysis of seven differe...cseij
According to World Health Organization (WHO), breast cancer is the top cancer in women both in the
developed and the developing world. Increased life expectancy, urbanization and adoption of western
lifestyles trigger the occurrence of breast cancer in the developing world. Most cancer events are
diagnosed in the late phases of the illness and so, early detection in order to improve breast cancer
outcome and survival is very crucial.
In this study, it is intended to contribute to the early diagnosis of breast cancer. An analysis on breast
cancer diagnoses for the patients is given. For the purpose, first of all, data about the patients whose
cancers’ have already been diagnosed is gathered and they are arranged, and then whether the other
patients are in trouble with breast cancer is tried to be predicted under cover of those data. Predictions of
the other patients are realized through seven different algorithms and the accuracies of those have been
given. The data about the patients have been taken from UCI Machine Learning Repository thanks to Dr.
William H. Wolberg from the University of Wisconsin Hospitals, Madison. During the prediction process,
RapidMiner 5.0 data mining tool is used to apply data mining with the desired algorithms.
A Novel Approach for Cancer Detection in MRI Mammogram Using Decision Tree In...CSCJournals
An intelligent computer-aided diagnosis system can be very helpful for radiologist in detecting and diagnosing microcalcifications’ patterns earlier and faster than typical screening programs. In this paper, we present a system based on fuzzy-C Means clustering and feature extraction techniques using texture based segmentation and genetic algorithm for detecting and diagnosing micro calcifications’ patterns in digital mammograms.We have investigated and analyzed a number of feature extraction techniques and found that a combination of three features, such as entropy, standard deviation, and number of pixels, is the best combination to distinguish a benign micro calcification pattern from one that is malignant. A fuzzy C Means technique in conjunction with three features was used to detect a micro calcification pattern and a neural network to classify it into benign/malignant. The system was developed on a Windows platform. It is an easy to use intelligent system that gives the user options to diagnose, detect, enlarge, zoom, and measure distances of areas in digital mammograms. The present study focused on the investigation of the application of artificial intelligence and data mining techniques to the prediction models of breast cancer. The artificial neural network, decision tree,Fuzzy C Means, and genetic algorithm were used for the comparative studies and the accuracy and positive predictive value of each algorithm were used as the evaluation indicators. 699 records acquired from the breast cancer patients at the MIAS database, 9 predictor variables, and 1 outcome variable were incorporated for the data analysis followed by the 10-fold cross-validation. The results revealed that the accuracies of Fuzzy C Means were 0.9534 (sensitivity 0.98716 and specificity 0.9582), the decision tree model 0.9634 (sensitivity 0.98615, specificity 0.9305), the neural network model 0.96502 (sensitivity 0.98628, specificity 0.9473), the genetic algorithm model 0.9878 (sensitivity 1, specificity 0.9802). The accuracy of the genetic algorithm was significantly higher than the average predicted accuracy of 0.9612. The predicted outcome of the Fuzzy C Means model was higher than that of the neural network model but no significant difference was observed. The average predicted accuracy of the decision tree model was 0.9635 which was the lowest of all 4 predictive models. The standard deviation of the 10-fold cross-validation was rather unreliable. The results showed that the genetic algorithm described in the present study was able to produce accurate results in the classification of breast cancer data and the classification rule identified was more acceptable and comprehensible. Keywords: Fuzzy C Means, Decision Tree Induction, Genetic algorithm, data mining, breast cancer, rule discovery.
Breast cancer detection from histopathological images is done using deep learning and transfer learning techniques. Image processing is done for better accuracy. CNN and DenseNet-121 algorithms are used. 90.9 % accuracy is achieved using CNN and 88% accuracy is achieved using Transfer learning.
SVM &GA-CLUSTERING BASED FEATURE SELECTION APPROACH FOR BREAST CANCER DETECTIONijscai
Mortality leading among women in developed countries is breast cancer. Breast cancer is women's second most prominent cause of cancer mortality worldwide. In recent decades, women's high prevalence of breast cancer has risen dramatically. This paper discussed several data analysis methods used to detect breast cancer early. Breast cancer diagnosis distinguishes benign and malignant breast lumps. Using data processing tools, we tackled this disease analysis. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. Several clinical breast cancer studies were conducted using soft computing and machine learning techniques. Sometimes their algorithms are easier, easier, or more comprehensive than others. This research is focused on genetic programming and machine learning algorithms to reliably identify benign and malignant breast cancer. This study aimed to optimise the testing algorithm. We used genetic programming methods to choose classification machines' best features and parameter values. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. We are analysing data accessible from the U.C.I. deep-learning data set in Wisconsin. In this experiment, we equate four Weka clustering strategies with genetic clustering. A comparison of results reveals that sequential minimal optimization (S.M.O.) is better than I.B.K. and B.F. Tree processes, i.e. 97.71%.
A Novel and Efficient Lifting Scheme based Super Resolution Reconstruction fo...CSCJournals
Mammography is the most effective method for early detection of breast diseases. However, the typical diagnostic signs, such as masses and microcalcifications, are difficult to be detected because mammograms are low contrast and noisy images. We concentrate on a special case of super resolution reconstruction for early detection of cancer from low resolution mammogram images. Super resolution reconstruction is the process of combining several low resolution images into a single higher resolution image. This paper describes a novel approach for enhancing the resolution of mammographic images. We are proposing an efficient lifting wavelet based denoising with adaptive interpolation for super resolution reconstruction. Under this frame work, the digitized low resolution mammographic images are decomposed into many levels to obtain different frequency bands. We use Daubechies (D4) lifting schemes to decompose low resolution mammogram images into multilevel scale and wavelet coefficients. Then our proposed novel soft thresholding technique is used to remove the noisy coefficients, by fixing optimum threshold value. In order to obtain an image of higher resolution adaptive interpolation is applied. Our proposed lifting wavelet transform based restoration and adaptive interpolation preserves the edges as well as smoothens the image without introducing artifacts. The proposed algorithm avoids the application of iterative method, reduces the complexity of calculation and applies to large dimension low-resolution images. Experimental results show that the proposed approach has succeeded in obtaining a high-resolution mammogram image with a high PSNR, ISNR ratio and a good visual quality.
SVM &GA-CLUSTERING BASED FEATURE SELECTION APPROACH FOR BREAST CANCER DETECTIONijscai
Mortality leading among women in developed countries is breast cancer. Breast cancer is women's second most prominent cause of cancer mortality worldwide. In recent decades, women's high prevalence of breast cancer has risen dramatically. This paper discussed several data analysis methods used to detect breast cancer early. Breast cancer diagnosis distinguishes benign and malignant breast lumps. Using data processing tools, we tackled this disease analysis. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. Several clinical breast cancer studies were conducted using soft computing and machine learning techniques. Sometimes their algorithms are easier, easier, or more comprehensive than others. This research is focused on genetic programming and machine
learning algorithms to reliably identify benign and malignant breast cancer. This study aimed to optimise the testing algorithm. We used genetic programming methods to choose classification machines' best features and parameter values. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. We are analysing data accessible from the U.C.I. deep-learning data
set in Wisconsin. In this experiment, we equate four Weka clustering strategies with genetic clustering. A comparison of results reveals that sequential minimal optimization (S.M.O.) is better than I.B.K. and B.F. Tree processes, i.e. 97.71%.
Sigma Xi Student Showcase - Fast Pre-Diagnosis of Breast Cancer Using CNNsMichael Batavia
My entry for the Sigma Xi Student Research Showcase
-- My research diagnosing benign vs malignant breast cancer tumors in the lymph node using a custom CNN + finding the best magnification for optimal accuracy.
A Novel Approach for Cancer Detection in MRI Mammogram Using Decision Tree In...CSCJournals
An intelligent computer-aided diagnosis system can be very helpful for radiologist in detecting and diagnosing microcalcifications’ patterns earlier and faster than typical screening programs. In this paper, we present a system based on fuzzy-C Means clustering and feature extraction techniques using texture based segmentation and genetic algorithm for detecting and diagnosing micro calcifications’ patterns in digital mammograms.We have investigated and analyzed a number of feature extraction techniques and found that a combination of three features, such as entropy, standard deviation, and number of pixels, is the best combination to distinguish a benign micro calcification pattern from one that is malignant. A fuzzy C Means technique in conjunction with three features was used to detect a micro calcification pattern and a neural network to classify it into benign/malignant. The system was developed on a Windows platform. It is an easy to use intelligent system that gives the user options to diagnose, detect, enlarge, zoom, and measure distances of areas in digital mammograms. The present study focused on the investigation of the application of artificial intelligence and data mining techniques to the prediction models of breast cancer. The artificial neural network, decision tree,Fuzzy C Means, and genetic algorithm were used for the comparative studies and the accuracy and positive predictive value of each algorithm were used as the evaluation indicators. 699 records acquired from the breast cancer patients at the MIAS database, 9 predictor variables, and 1 outcome variable were incorporated for the data analysis followed by the 10-fold cross-validation. The results revealed that the accuracies of Fuzzy C Means were 0.9534 (sensitivity 0.98716 and specificity 0.9582), the decision tree model 0.9634 (sensitivity 0.98615, specificity 0.9305), the neural network model 0.96502 (sensitivity 0.98628, specificity 0.9473), the genetic algorithm model 0.9878 (sensitivity 1, specificity 0.9802). The accuracy of the genetic algorithm was significantly higher than the average predicted accuracy of 0.9612. The predicted outcome of the Fuzzy C Means model was higher than that of the neural network model but no significant difference was observed. The average predicted accuracy of the decision tree model was 0.9635 which was the lowest of all 4 predictive models. The standard deviation of the 10-fold cross-validation was rather unreliable. The results showed that the genetic algorithm described in the present study was able to produce accurate results in the classification of breast cancer data and the classification rule identified was more acceptable and comprehensible. Keywords: Fuzzy C Means, Decision Tree Induction, Genetic algorithm, data mining, breast cancer, rule discovery.
Breast cancer detection from histopathological images is done using deep learning and transfer learning techniques. Image processing is done for better accuracy. CNN and DenseNet-121 algorithms are used. 90.9 % accuracy is achieved using CNN and 88% accuracy is achieved using Transfer learning.
SVM &GA-CLUSTERING BASED FEATURE SELECTION APPROACH FOR BREAST CANCER DETECTIONijscai
Mortality leading among women in developed countries is breast cancer. Breast cancer is women's second most prominent cause of cancer mortality worldwide. In recent decades, women's high prevalence of breast cancer has risen dramatically. This paper discussed several data analysis methods used to detect breast cancer early. Breast cancer diagnosis distinguishes benign and malignant breast lumps. Using data processing tools, we tackled this disease analysis. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. Several clinical breast cancer studies were conducted using soft computing and machine learning techniques. Sometimes their algorithms are easier, easier, or more comprehensive than others. This research is focused on genetic programming and machine learning algorithms to reliably identify benign and malignant breast cancer. This study aimed to optimise the testing algorithm. We used genetic programming methods to choose classification machines' best features and parameter values. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. We are analysing data accessible from the U.C.I. deep-learning data set in Wisconsin. In this experiment, we equate four Weka clustering strategies with genetic clustering. A comparison of results reveals that sequential minimal optimization (S.M.O.) is better than I.B.K. and B.F. Tree processes, i.e. 97.71%.
A Novel and Efficient Lifting Scheme based Super Resolution Reconstruction fo...CSCJournals
Mammography is the most effective method for early detection of breast diseases. However, the typical diagnostic signs, such as masses and microcalcifications, are difficult to be detected because mammograms are low contrast and noisy images. We concentrate on a special case of super resolution reconstruction for early detection of cancer from low resolution mammogram images. Super resolution reconstruction is the process of combining several low resolution images into a single higher resolution image. This paper describes a novel approach for enhancing the resolution of mammographic images. We are proposing an efficient lifting wavelet based denoising with adaptive interpolation for super resolution reconstruction. Under this frame work, the digitized low resolution mammographic images are decomposed into many levels to obtain different frequency bands. We use Daubechies (D4) lifting schemes to decompose low resolution mammogram images into multilevel scale and wavelet coefficients. Then our proposed novel soft thresholding technique is used to remove the noisy coefficients, by fixing optimum threshold value. In order to obtain an image of higher resolution adaptive interpolation is applied. Our proposed lifting wavelet transform based restoration and adaptive interpolation preserves the edges as well as smoothens the image without introducing artifacts. The proposed algorithm avoids the application of iterative method, reduces the complexity of calculation and applies to large dimension low-resolution images. Experimental results show that the proposed approach has succeeded in obtaining a high-resolution mammogram image with a high PSNR, ISNR ratio and a good visual quality.
SVM &GA-CLUSTERING BASED FEATURE SELECTION APPROACH FOR BREAST CANCER DETECTIONijscai
Mortality leading among women in developed countries is breast cancer. Breast cancer is women's second most prominent cause of cancer mortality worldwide. In recent decades, women's high prevalence of breast cancer has risen dramatically. This paper discussed several data analysis methods used to detect breast cancer early. Breast cancer diagnosis distinguishes benign and malignant breast lumps. Using data processing tools, we tackled this disease analysis. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. Several clinical breast cancer studies were conducted using soft computing and machine learning techniques. Sometimes their algorithms are easier, easier, or more comprehensive than others. This research is focused on genetic programming and machine
learning algorithms to reliably identify benign and malignant breast cancer. This study aimed to optimise the testing algorithm. We used genetic programming methods to choose classification machines' best features and parameter values. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. We are analysing data accessible from the U.C.I. deep-learning data
set in Wisconsin. In this experiment, we equate four Weka clustering strategies with genetic clustering. A comparison of results reveals that sequential minimal optimization (S.M.O.) is better than I.B.K. and B.F. Tree processes, i.e. 97.71%.
Sigma Xi Student Showcase - Fast Pre-Diagnosis of Breast Cancer Using CNNsMichael Batavia
My entry for the Sigma Xi Student Research Showcase
-- My research diagnosing benign vs malignant breast cancer tumors in the lymph node using a custom CNN + finding the best magnification for optimal accuracy.
Modified fuzzy rough set technique with stacked autoencoder model for magneti...IJECEIAES
Breast cancer is the common cancer in women, where early detection reduces the mortality rate. The magnetic resonance imaging (MRI) images are efficient in analyzing breast cancer, but it is hard to identify the abnormalities. The manual breast cancer detection in MRI images is inefficient; therefore, a deep learning-based system is implemented in this manuscript. Initially, the visual quality improvement is done using region growing and adaptive histogram equalization (AHE), and then, the breast lesion is segmented by Otsu thresholding with morphological transform. Next, the features are extracted from the segmented lesion, and a modified fuzzy rough set technique is proposed to reduce the dimensions of the extracted features that decreases the system complexity and computational time. The active features are fed to the stacked autoencoder for classifying the benign and malignant classes. The results demonstrated that the proposed model attained 99% and 99.22% of classification accuracy on the benchmark datasets, which are higher related to the comparative classifiers: decision tree, naïve Bayes, random forest and k-nearest neighbor (KNN). The obtained results state that the proposed model superiorly screens and detects the breast lesions that assists clinicians in effective therapeutic intervention and timely treatment.
The current big challenge facing radiologists in healthcare is the automatic detection and classification of masses in breast mammogram images. In the last few years, many researchers have proposed various solutions to this problem. These solutions are effectively dependent and work on annotated breast image data. But these solutions fail when applied to unlabeled and non-annotated breast image data. Therefore, this paper provides the solution to this problem with the help of a neural network that considers any kind of unlabeled data for its procedure. In this solution, the algorithm automatically extracts tumors in images using a segmentation approach, and after that, the features of the tumor are extracted for further processing. This approach used a double thresholding-based segmentation technique to obtain a perfect location of the tumor region, which was not possible in existing techniques in the literature. The experimental results also show that the proposed algorithm provides better accuracy compared to the accuracy of existing algorithms in the literature.
Breast cancer histological images nuclei segmentation and optimized classifi...IJECEIAES
Breast cancer incidences have grown worldwide during the previous few years. The histological images obtained from a biopsy of breast tissues are regarded as being the highest accurate approach to determine whether any cells exhibit symptoms of cancer. The visible position of nuclei inside the image is achieved through the use of instance segmentation, nevertheless, this work involves nucleus segmentation and features classification of the predicted nucleus for the achievement of best accuracy. The extracted features map using the feature pyramid network has been modified using segmenting objects by locations (SOLO) convolution with grasshopper optimization for multiclass classification. A breast cancer multiclassification technique based on a suggested deep learning algorithm was examined to achieve the accuracy of 99.2% using a huge database of ICIAR 2018, demonstrating the method’s efficacy in offering an important weapon for breast cancer multi-classification in a medical setting. The segmentation accuracy achieved is 88.46%.
Objective Quality Assessment of Image Enhancement Methods in Digital Mammogra...sipij
Breast cancer is the most common cancer among women worldwide constituting more than 25%
of all cancer incidences occurring in the world [1]. Statistics show that US, India and China
account for more than one third of all breast cancer cases [2]. Also, there has been a steady
increase in the breast cancer incidence among young generation in the world. In India, one out of
two women die after being detected with breast cancer where as in China it is one in four and in
USA it is one in eight [2]. Therefore, the statistics show that cancer mortality is highest in India
among all other nations in the world. In US, though the number of women diagnosed with cancer
is more than that in India, their mortality
Breast cancer classification with histopathological image based on machine le...IJECEIAES
Breast cancer represents one of the most common reasons for death in the worldwide. It has a substantially higher death rate than other types of cancer. Early detection can enhance the chances of receiving proper treatment and survival. In order to address this problem, this work has provided a convolutional neural network (CNN) deep learning (DL) based model on the classification that may be used to differentiate breast cancer histopathology images as benign or malignant. Besides that, five different types of pre-trained CNN architectures have been used to investigate the performance of the model to solve this problem which are the residual neural network-50 (ResNet-50), visual geometry group-19 (VGG-19), Inception-V3, and AlexNet while the ResNet-50 is also functions as a feature extractor to retrieve information from images and passed them to machine learning algorithms, in this case, a random forest (RF) and k-nearest neighbors (KNN) are employed for classification. In this paper, experiments are done using the BreakHis public dataset. As a result, the ResNet-50 network has the highest test accuracy of 97% to classify breast cancer images.
Cervical cancer diagnosis based on cytology pap smear image classification us...TELKOMNIKA JOURNAL
Doctors and pathologists have long been concerned about determining the malignancy from cell images. This task is laborious, time-consuming and needs expertise. Due to this reason, automated systems assist pathologists in providing a second opinion to arrive at accurate decision based on cytology images. The classification of cytology images has always been a difficult challenge among the various image analysis approaches due to its extreme intricacy. The thrust for early diagnosis of cervical cancer has always fuelled the research in medical image analysis for cancer detection. In this paper,
an investigative study for the classification of cytology images is proposed.
The proposed study uses the discrete coefficient transform (DCT) coefficient and Haar transform coefficients as features. These features are given as a input to seven different machine learning algorithms for normal and abnormal pap smear images classification. In order to optimize the feature size, fractional coefficients are used to form the five different sizes of feature vectors. In the proposed work, DCT transform has given the highest classification accuracy of 81.11%. Comparing the different machine learning algorithms the overall best performance is given by the random forest classifier.
Breast cancer is the leading cause of death for women worldwide. Cancer can be discovered early, lowering the rate of death. Machine learning techniques are a hot field of research, and they have been shown to be helpful in cancer prediction and early detection. The primary purpose of this research is to identify which machine learning algorithms are the most successful in predicting and diagnosing breast cancer, according to five criteria: specificity, sensitivity, precision, accuracy, and F1 score. The project is finished in the Anaconda environment, which uses Python's NumPy and SciPy numerical and scientific libraries as well as matplotlib and Pandas. In this study, the Wisconsin diagnostic breast cancer dataset was used to evaluate eleven machine learning classifiers: decision tree, quadratic discriminant analysis, AdaBoost, Bagging meta estimator, Extra randomized trees, Gaussian process classifier, Ridge, Gaussian nave Bayes, k-Nearest neighbors, multilayer perceptron, and support vector classifier. During performance analysis, extremely randomized trees outperformed all other classifiers with an F1-score of 96.77% after data collection and data analysis.
Automated breast cancer detection system from breast mammogram using deep neu...nooriasukmaningtyas
All over the world breast cancer is a major disease which mostly affects the women and it may also cause death if it is not diagnosed in its early stage. But nowadays, several screening methods like magnetic resonance imaging (MRI), ultrasound imaging, thermography and mammography are available to detect the breast cancer. In this article mammography images are used to detect the breast cancer. In mammography image the cancerous lumps/microcalcifications are seen to be tiny with low contrast therefore it is difficult for the doctors/radiologist to detect it. Hence, to help the doctors/radiologist a novel system based on deep neural network is introduced in this article that detects the cancerous lumps/microcalcifications automatically from the mammogram images. The system acquires the mammographic images from the mammographic image analysis society (MIAS) data set. After pre-processing these images by 2D median image filter, cancerous features are extracted from the images by the hybridization of convolutional neural network with rat swarm optimization algorithm. Finally, the breast cancer patients are classified by integrating random forest with arithmetic optimization algorithm. This system identifies the breast cancer patients accurately and its performance is relatively high compared to other approaches.
A Review on Data Mining Techniques for Prediction of Breast Cancer RecurrenceDr. Amarjeet Singh
The most common type of cancer in women
worldwide is the Breast Cancer. Breast cancer may be
detected early using Mammograms, probably before it's
spread. Recurrent breast cancer could occur months or years
after initial treatment. The cancer could return within the
same place because the original cancer (local recurrence), or it
may spread to different areas of your body (distant
recurrence). Early stage treatment is done not only to cure
breast cancer however additionally facilitate in preventing its
repetition/recurrence. Data mining algorithms provide
assistance in predicting the early-stage breast cancer that
continually has been difficult analysis drawback. The
projected analysis can establish the most effective algorithm
that predicts the recurrence of the breast cancer and improve
the accuracy the algorithms. Large information like Clump,
Classification, Association Rules, Prediction and Neural
Networks, Decision Trees can be analyzed using data mining
applications and techniques.
SVM &GA-CLUSTERING BASED FEATURE SELECTION APPROACH FOR BREAST CANCER DETECTIONijscai
Mortality leading among women in developed countries is breast cancer. Breast cancer is women's second
most prominent cause of cancer mortality worldwide. In recent decades, women's high prevalence of breast
cancer has risen dramatically. This paper discussed several data analysis methods used to detect breast
cancer early. Breast cancer diagnosis distinguishes benign and malignant breast lumps. Using data
processing tools, we tackled this disease analysis. Data mining is an important step of library discovery
where intelligent methods are used to detect patterns. Several clinical breast cancer studies were
conducted using soft computing and machine learning techniques. Sometimes their algorithms are easier,
easier, or more comprehensive than others. This research is focused on genetic programming and machine
learning algorithms to reliably identify benign and malignant breast cancer. This study aimed to optimise
the testing algorithm. We used genetic programming methods to choose classification machines' best
features and parameter values. Data mining is an important step of library discovery where intelligent
methods are used to detect patterns. We are analysing data accessible from the U.C.I. deep-learning data
set in Wisconsin. In this experiment, we equate four Weka clustering strategies with genetic clustering. A
comparison of results reveals that sequential minimal optimization (S.M.O.) is better than I.B.K. and B.F.
Tree processes, i.e. 97.71%.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
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CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
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R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
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Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
2. 348 H. Li et al. / Biomedical Signal Processing and Control 51 (2019) 347–354
Fig. 1. Benign and malignant breast mass images.
MIAS database, and the author pointed out that the classification
accuracy of the masses reached 95% [7]. Combining regression fea-
tures to eliminate SVM and normalized mutual information feature
selection method, Liu et al. simulated the mammogram images of
DDSM library, and the results showed that the classification accu-
racy of the method reached 93% [8]. However, these methods had
inherent disadvantages, that is, the traditional SVM assumed that
all features had the same degree of influence on the classifica-
tion target. Since most of the above classification algorithms were
carried out on small data sets, they were not suitable for identify-
ing and classifying large data sets in hospitals. In addition, there
were no uniform comparison between algorithms, and the accu-
racy was not comparable. Moreover, these classification algorithms
used artificial-based feature extraction methods, which not only
required professional domain knowledge, but also required a lot
of time and effort to complete. The key was that there were often
certain difficulties in extracting features. It seriously restricted the
application of traditional machine learning algorithms in mammo-
gram images classification.
On the other hand, neural networks based on deep learning
classify mammograms. Arbach et al. evaluated the performance of
classification of mammograms based on backpropagation neural
network (BNN) and compared them with radiologists and resi-
dents. The results showed that the area under the ROC curve of BNN
was Az = 0.923.The area under the ROC curve of the radiologist was
Az = 0.864, and the area under the ROC curve of the resident was
Az = 0.648 [9]. The neural network system designed by Shih-Chung
et al. detected 91 cases of mammogram images. The test resulted
that the area under the ROC curve was Az = 0.78 [10]. Moreover, the
hidden layer of the neural network they designed has 125 nodes,
which maked the generalization of the neural network not good
enough. The common features of these methods were higher sensi-
tivity, lower specificity, lower recognition rate, and greater research
space.
Therefore, this paper studies and improves a deeper and more
complex DenseNet neural network model [11], inventing a new
DenseNet-II neural network model, which can maintain the sparse-
ness of the network structure. It also prevents the model from
overfitting and improves computer performance. At the same time,
the data enhancement method is used to prevent the over-fitting
caused by the small sample data set, bring about an increase
in rate of identification of the mammogram images. Using the
DenseNet-II neural network model can not only improve the diag-
nostic efficiency, but also provide doctors with more objective and
accurate diagnosis results. so it has important clinical application
value.
2. Data collection and preprocessing
2.1. Data collection
The datasets used in this paper are taken from mammography
images provided by the First Hospital of Shanxi Medical University.
Mammary gland lesions are obtained by full-field digital mammog-
raphy (FFDM) examination. A total of 2042 cases of breast disease
are usually defined as positive for malignancy and negative for
benign [12]. After being confirmed by the experts of the First Hos-
pital of Shanxi Medical University, there are 1011 negative cases
and 1031 positive cases, all of which are female patients.All of the
mammogram images in the database have been marked by experts,
as shown in Fig. 1(a) and (b) are images of malignant breast tumors
and benign breast tumors, respectively.
2.2. Data preprocessing
Mammography is currently the only widely accepted means of
routine breast cancer screening [13] to assist physicians in clinical
diagnosis. However, it is a low-radiation, high-resolution image. It
is difficult to judge the mammogram images directly with the naked
eye. Therefore, it is necessary to preprocess the image datasets.
Through image preprocessing, the quality and quantity of images
can be improved, making the recognition rate more accurate.
Two processing techniques are used for the mammogram images:
zero-mean normalization [14] and data enhancement, which can
improve the speed and accuracy of mammogram images classifica-
tion.
2.2.1. Zero-mean normalization
The main purpose of image normalization [15] is to reduce the
interference of medical images due to uneven light. Image nor-
malization causes the image to find some invariants, enhances
robustness, and speeds up the convergence of the training model.
Zero-mean normalization is the normalization of data for the mean
and standard deviation of the raw data. The processed data con-
forms to the standard normal distribution, that is, the mean is 0, the
standard deviation is 1, and the conversion function is as shown in
(1):
x∗
=
x −
(1)
Where is the mean of all sample data, is the standard deviation
of all sample data.
3. H. Li et al. / Biomedical Signal Processing and Control 51 (2019) 347–354 349
Fig. 2. DenseNet network dense connection mechanism (where c represents the channel level connection operation).
2.2.2. Dataset enhancements
The manually labeled training data set is limited. Due to the
limitation of the training samples, CNN is prone to over-fitting
during the training process, resulting in low medical image recog-
nition rate and unsatisfactory diagnosis results [16]. Therefore, this
requires a large increase in the training data set. The solution is to
enhance the mammogram images dataset by affine transformation
and random cropping.
(1) The mammogram images data set is enhanced by the affine
transformation method [17]. It mainly rotates the image by 90 ◦
/ 180 ◦ / 270 degrees, scales by 0.8, mirrors horizontally and
vertically, and combines these operations.
(2) Mammogram images are processed by random cropping to
obtain more data sets. The data set can be expanded by a factor
of 15 by the above two methods.
3. Methods
With the continuous improvement of computer computing
power and the arrival of the era of big data, deep learning over-
comes the limitations of shallow learning, without the need to
manually design and extract features. It can extract more expres-
sive and abundant essential characteristics from the data set. In
recent years, deep learning has been successfully applied in the
fields of computer vision, natural language processing, medical
image processing, etc. [18]. Therefore, the use of deep learning
technology for the identification of benign and malignant breast
tumors has become the focus of clinical diagnosis research, and the
accurate identification of benign and malignant breast tumors has
important research significance for the diagnosis and treatment of
breast cancer [19].
Deep learning can be regarded as a multi-layer artificial neu-
ral network [20]. By constructing a neural network model with
multiple hidden layers, the low-level features are transformed
by layer-by-layer nonlinear features to form a more abstract
high-level feature expression to discover the distributed feature
representation of the data [21]. As one of the most commonly
used deep learning models, convolutional neural networks directly
use 2D or 3D images as input to the network, avoiding the com-
plex feature extraction process in traditional machine learning
algorithms. Compared with a fully connected neural network, con-
volutional neural networks use local connections, weight sharing,
and downsampling, reducing the number of network parameters
and reducing computational complexity. At the same time, the
translation, zoom, rotation and other changes of the image are
highly invariant.
3.1. DenseNet neural network model
This paper chooses the densely connected DenseNet neu-
ral network model. Different from other convolutional neural
networks, it has the following two characteristics:(1) Dense con-
nection: Each layer is connected to each of the previous layers to
achieve feature reuse.(2) Due to feature reuse, each layer uses a
small number of convolution kernel extraction features to achieve
the purpose of reducing redundancy. In DenseNet, each layer
is concated with all previous layers in the channel dimension,
and for an L-layer network, DenseNet contains a total of
L(L+1)
2
connections. Fig. 2 shows the dense connection mechanism of
DenseNet.
The advantages of DenseNet are mainly reflected in the follow-
ing aspects:
(1) Effectively solve the problem of gradient disappearance;
(2) Strengthening feature propagation;
(3) Support feature reuse;
(4) Significantly reduce the number of parameters.
This model can omit the pre-training on the ImageNet [22]
dataset and start training directly from the randomly initial-
ized model, achieving the goal of saving time and efficiency. In
many large-scale practical applications, there are great differences
between the actual dataset and the ImageNet dataset. Therefore,
it is a good prospect to apply this network model that does not
require pre-training to medical image classification [23].
3.1.1. Design of DenseNet neural network structure
DenseNet’s network structure consists mainly of the DenseBlock
layer and the Transition layer. In order to prevent the network
structure from becoming wider, the total number of DenseNet
networks is 40 layers, and the growth rate k = 12, including 3
DenseBlock layers and 2 Transition layers. This model does not
have too many parameters, while saving computational memory
and reducing overfitting. As shown in Fig. 3.
3.1.1.1. DenseBlock layer. In all DenseBlocks, each layer is out-
putted with a k-characteristic map after convolution (and the
feature maps of each layer are the same size), that is, k convo-
lution kernels are used for feature extraction. k is called growth
rate in DenseNet, which is a hyperparameter. Since there are many
inputs for each layer in the back, DenseBlock can use the bot-
tleneck layer internally to reduce the amount of computation,
that is, th BN + ReLU+ 1 × 1 Conv + BN + ReLU+ 3 × 3 Conv struc-
ture used in the nonlinear combination function in DenseBlock. As
shown in Fig. 4. The role of 1 × 1 Conv is to reduce the number
of features, which can improve the calculation speed and effi-
ciency.
3.1.1.2. Transition layer. It mainly connects two adjacent Dense-
Block layers and reduces the feature map size. The Transition layer
includes a 1 × 1 convolution and 2 × 2 AvgPooling [24], and the
structure is BN + ReLU+ 1 × 1 Conv+ 2 × 2 AvgPooling. Therefore,
4. 350 H. Li et al. / Biomedical Signal Processing and Control 51 (2019) 347–354
Fig. 3. DenseNet neural network structure.
Fig. 4. DenseBlock using the bottleneck layer.
the Transition layer can function as a compression model. Each
layer will connect all the previous layers as input:
xl = Hl ([x0, x1, . . ., xl−1]) (2)
H1(•) is a non-linear transformation function, a combination that
includes a series of BN(Batch Normalization), ReLU, Pooling, and
Conv operations. The final DenseBlock is followed by a global Avg-
Pooling layer, which is then sent to a softmax classifier for image
recognition classification.
3.2. DenseNet-II neural network structure
Due to the deeper and wider network model, the defects
of huge parameters are generated, which leads to over-fitting,
increasing the amount of calculation, and consuming more com-
puting resources. To avoid this problem, the DenseNet model has
been improved and is called the DenseNet-II network model. The
Inception structure [25] replaces the first3 × 3 convolution in the
DenseNet network before entering the first DenseBlock layer. The
improved model is called the DenseNet-II neural network model.
Like the DenseNet neural network model, the DenseNet-II neu-
ral network model uses 40 layers with a growth rate of k = 12,
including 3 DenseBlock layers and 2 Transition layers. As shown in
Fig. 5.
The main differences between DenseNet and DenseNet-II neural
network structures are:
For the DenseNet neural network, before entering the first
DenseBlock, first perform a 3 × 3 convolution (stride = 1). There are
a total of 16 convolution kernels. The convolution layer is mainly
responsible for extracting features in the image, avoiding the mis-
take of manually extracting features. Consisting of a set of feature
maps, the same feature map shares a convolution kernel, which is
actually a set of weights, also called filters. A learnable convolu-
tion kernel is convolved with several feature maps of the previous
layer, and the corresponding elements are accumulated and then
offset, and then passed to a nonlinear activation function ReLU func-
tion to obtain a feature map, that is, the extraction of a feature is
achieved. A plurality of different convolution kernels enable extrac-
tion of multiple features. The calculation formula is as shown in
(3)
x
(l)
j
= f
i ∈ Ml−1
x
(l−1)
i
∗ k
(l)
ij
+ b
(l)
j
(3)
Where l represents the number of layers, and kij represents the
convolution kernel of the feature map j connecting the lth layer
and the feature map i of the l-1th layer, Ml−1 represents the input
feature maps selected by the l-1layer, and * represents the convolu-
tion operation, b denotes the offset, and f (•)denotes the nonlinear
activation function.
For the DenseNet-II neural network, the Inception structure is
mainly used to replace the first 3×3 convolutional layer of the
DenseNet neural network model. Because a deeper and wider net-
work structure can obtain better predictive recognition, it will
generate huge parameters, which will lead to over-fitting, which
greatly increases the amount of calculation and consumes more
computing resources. Inception mainly improves the traditional
convolutional layer in the network. For deep neural network per-
formance and computation, it mainly solves the following problems
in deep networks:
(1) too many parameters, easy to over-fit, if the training data set is
limited;
(2) The larger the network, the more computational complexity it
is, and it is difficult to apply;
(3) The deeper the network, the easier it is for the gradient to disap-
pear backwards(gradient dispersion), it is difficult to optimize
the model.
The inception structure: not only maintains the sparseness of
the network structure, but also utilizes the high computational
performance of dense matrices. In literature [24], the Inception
module structure is mainly proposed (1 × 1,3 × 3,5 × 5 conv and
3 × 3 pooling combined), as shown in Fig. 6. The basic structure of
the Inception module: There are 4 branches, each with a 1 × 1 con-
volution. The 1 × 1 convolution is a very useful structure, which can
organize information across channels, improve the expressiveness
5. H. Li et al. / Biomedical Signal Processing and Control 51 (2019) 347–354 351
Fig. 5. The structure of DenseNet-II neural network model.
Fig. 6. The inception model.
of the network, and also raising dimension and dimension of the
output channel. The module has 3 different sizes of convolution and
1 maximum pooling, which increases the adaptability of the net-
work to different scales. It can expand the depth and width of the
network with high efficiency, and improve the accuracy without
causing over-fitting.
Therefore, the overall structure of the DenseNet-II neural net-
work mainly includes Inception, Dense Blocks and transition layers,
with fewer parameters and higher precision.
In the DenseNet and the DenseNet-II neural network model, all
3 × 3 3 × 3 convolutions use padding = 1 to ensure that the fea-
ture map size remains unchanged. All convolutions use the ReLU
function.
4. Analysis of the experiment and results
4.1. Experimental network parameter settings
This model is based on the Ubantu 18.04 operating system,
using two NVIDIA Titan X graphics cards, and experimenting on
the pytorch framework. The data enhancement algorithm is imple-
mented in python.
In order to test the classification performance of the DenseNet-
II model, the AlexNet, VGGNet, GoogLeNet and DenseNet network
models are compared with the DenseNet-II model for experimental
analysis. The network structure of the selected AlexNet, VGGNet
and GoogLeNet models are as follows:
(1) The AlexNet model [26] has eight layers and has a parameter
amount of 60 M or more. The first five layers are convolutional
layers, the last three layers are fully connected layers, and the
output of the last fully connected layer has 1000 outputs. The
network structure is shown in Fig. 7.
(2) The VGGNet [27] model established a 19-layer deep network,
and achieved the first and second classification results in
ILSVRC. The VGGNet network model has many similarities with
the AlexNet framework. There are sixteen convolutional lay-
ers, two layers of fully connected image feature layers, and
one layer of fully connected classification feature layers. The
network structure is shown in Fig. 8.
(3) The GoogLeNet model [28] is a 22-layer deep network. Its
biggest feature is the introduction of the Inception struc-
ture, and solves the problem of huge network parameters and
resource consumption. The GoogLeNet model uses the Incep-
tion structure, which not only further improves the accuracy of
the classification, but also greatly reduces the amount of param-
eters. The GoogLeNet network structure is shown in Table 1.
In addition, due to the experimental evaluation of benign and
malignant breast tumors, in the AlexNet network and GoogLeNet
model, the full-connected layer output of the convolutional neural
network was changed from 1000 to 2, which became a two-class
problem. The initial learning rates of the five models are all set to
0.01, and the maximum number of iterations is 8000.
4.2. Training strategy
Cross-validation is a method of model selection by estimating
the generalization error of the model [29]. It does not have any
assumptions, it is an effective model selection method, which has
the universality of application and easy operation. In order to accu-
rately measure the quality of a model evaluation method, this paper
uses K-fold cross-validation [30], K is 10.
In this paper, the data collected from the First Hospital of Shanxi
Medical University was expanded 15 times after image preprocess-
ing, and 30,630 pairs of mammogram images are obtained. Among
them, 15,165 pairs of benign tumor images and 15,645 pairs of
malignant tumor images. Using a 10-fold cross-validation method,
all data sets were randomly divided into 10 disjoint and identi-
6. 352 H. Li et al. / Biomedical Signal Processing and Control 51 (2019) 347–354
Fig. 7. AlexNet network structure.
Table 1
GoogLeNet network structure.
type size/stride output depth 1×1 3×3 3×3 3×3 3×3 pooling
Conv 7×7/2 112×112×64 1
max pool 3×3/2 56×56×64 0
Conv 3×3/1 56×56×192 2 64 192
max pool 3×3/2 28×28×192 0
Inception(3a) 28×28×256 2 64 96 128 16 32 32
Inception(3b) 28×28×480 2 128 128 192 32 96 64
max pool 3×3/2 14×14×480 0
Inception(4a) 14×14×512 2 192 96 208 16 96 64
Inception(4b) 14×14×512 2 160 112 224 24 64 64
Inception(4c) 14×14×512 2 128 128 256 24 64 64
Inception(4d) 14×14×528 2 112 144 288 32 64 64
Inception(4e) 14×14×832 2 256 160 320 32 128 128
max pool 3×3/2 7×7×832 0
Inception(5a) 7×7×832 2 256 160 320 32 128 128
Inception(5b) 7×7×1024 2 384 192 384 48 128 128
Avg pool 7×7/1 1×1×1024 0
Dropout40% 1×1×1024 0
fc 1×1×1000 1
softmax 1×1×1000 0
cally sized subsets. Each time a subset is taken as the test data set,
and the remaining 9 samples are taken as the training data set, of
which 25% of the training set is used as the verification set. Until all
10 subsets have been tested once, that is, the test set is cycled, the
cross-validation process ends. The average of the accuracy of the
10 test results was calculated as the overall evaluation index of the
model, and the model with the highest accuracy was obtained. Each
data set contains approximately 50% of benign cases and malig-
nant cases. Among them, the training set is used for model training
and parameter learning; the verification set is used to optimize the
model, and the parameters are automatically fine-tuned according
to the test results of the model in the training process; the test set
is used to test the model’s recognition and generalization ability.
Through the K-fold cross-validation principle, the assigned
30,630 mammogram data sets were placed in the AlexNet, VGGNet,
GoogLeNet, DenseNet and DenseNet-II models for training, ver-
ification and testing according to the unified principle. Different
network models use the time.time () function to calculate the time.
The difference between the end time and the start time is the time
when the network model is classified.
4.3. Evaluation criteria
For the classification of medical images, this paper evaluates
the classification performance of the model from sensitivity (Sen),
specificity (Spec), and accuracy (Acc) [31]. Specificity, sensitivity
and accuracy are defined as follows.
Sensitivity(Sen) =
TP
TP + FN
× 100% (4)
Specificity(Spec) =
TN
TN + FP
× 100% (5)
Accuracy (Acc) =
TP + TP
TP + TN + FP + FN
× 100% (6)
In the formula, TP (True positive) is the number of pictures which
diagnostic model can accurately identify as malignant tumors. FP
(False positive) refers to the number of pictures in which a diag-
nostic model mistakenly identifies a benign tumor as a malignant
tumor. TN (True negative) is the number of pictures that the diag-
nostic model can accurately identify as a benign tumor. FN (False
positive) refers to the number of pictures of a type of benign tumor
detected by a diagnostic model malignant tumor.
The receiver operating characteristic (ROC) can be further intro-
duced by TP and FP. The ROC curve is a graphical curve on a
two-dimensional plane. By using the performance of the classifi-
cation model, adjusting the threshold of the classifier, a curve of
(0,0), (1,1) can be obtained, which is the ROC curve of the classi-
fication model. The AUC value refers to the area enclosed by the
ROC curve in the [0,1] interval and the X axis. The overall perfor-
mance of the computer-aided diagnostic system is proportional to
the AUC value, which means the greater the AUC value, the better
the performance [32].
4.4. Experimental results and analysis
To verify the performance of the benign and malignant classifi-
cation methods of mammography in this paper, the training results
of the five different network models are all K-fold cross-validated,
and the K value is 10. The main evaluation classifier performance
indicators are sensitivity (Sen), specificity (Spec), accuracy (Acc)
and ROC curve area (AUC), etc. The result is a comparative anal-
ysis of the 10-fold cross-validation average results. The statistical
experiment results are shown in Table 2.
7. H. Li et al. / Biomedical Signal Processing and Control 51 (2019) 347–354 353
Fig. 8. VGG network structure.
It can be seen that when the improved DenseNet-II network
model is adopted, the three classification indicators of Spec, Sen
and Acc are the highest respectively. Compared with the other
four network models, the improved DenseNet-II network model
with Inception structure has been added, the average accuracy of
classification reaches 94.55%. It shows that the improved model
can extract more distinguishing features, so the recognition rate is
higher, and it has better robustness and generalization.
Table 2
The performance table of breast cancer benign and malignant classification(the 10-
fold cross-validation average results).
Neural Networks
Performance
Acc(%) Sen(%) Spec(%)
AlexNet 92.70 93.60 91.78
VGGNet 92.78 93.58 92.42
GooLeNet 93.54 93.90 93.17
DenseNet 93.87 94.59 93.90
DenseNet-II 94.55 95.60 95.36
Fig. 9. The training error rate of the five network models (the 10-fold cross-
validation average results).
Fig. 10. The ROC curve and AUC value of five network models (the 10-fold cross-
validation average results).
The training error rate of the five network models is shown in
Fig. 9. The abscissa indicates the number of iterations, and the ordi-
nate indicates the training error rate. As the number of training
increases, the overall trend is downward. The error rate of the
DenseNet-II model has been the lowest, followed by DenseNet,
GoogLeNet, and VGGNet, and finally AlexNet.
Fig.10 shows the ROC curve of the five network structure clas-
sification performance and the area under the curve AUC. The
coordinates are the false positive rate (1-Specificity) and the ordi-
8. 354 H. Li et al. / Biomedical Signal Processing and Control 51 (2019) 347–354
nate is the sensitivity (Sensitivity). The DenseNet-II model was
initially at a distinct advantage, followed by DenseNet model.
Among them, the VGCNet and GoogLeNet models have AUC val-
ues of more than 0.8, AlexNet has the worst classification effect,
and the AUC value reaches 0.778. It can also be seen that the clas-
sification performance of the DenseNet-II structure is significantly
better than other structural models. It can be seen that DenseNet-
II has better performance in mammography images classification.
With the increase of the number of iterations, the training error
rate is steadily reduced, and there is no over-fitting phenomenon.
Due to the advantage of dense connection between DenseNet
and DenseNet-II, the gradient disappearance problem is effectively
solved, the number of parameters is greatly reduced, and fea-
ture propagation and feature reuse are enhanced. Both models
have advantages over AlexNet, VGGNet and GoogLeNet in terms
of sensitivity, specificity, accuracy, and classification performance.
In addition, DenseNet-II has added the Inception structure, which
greatly reduces the amount of parameters and solves the problem
of gradient descent. The classification performance is better than
that of the DenseNet model, which further improves the accuracy of
the benign and malignant classification of mammography images.
5. Conclusion
This paper studies the use of deep learning methods to achieve
the benign and malignant classification of mammograms, and pro-
poses an improved neural network model called DenseNet-II neural
network model. Firstly, aiming at the insufficiency of sample data
set, through the data enhancement method, the over-fitting prob-
lems in deep learning due to insufficient data set is effectively
avoided. Secondly, Inception Net replaces the first convolutional
layer of the DenseNet neural network model. The DenseNet neural
network model is improved, and the calculation speed and effi-
ciency of the network model are improved. Finally, the 10-fold
cross-validation is used to verify the classification results of five
network models. The results show that DenseNet-II neural net-
work model is superior to other structural models in classification
performance, thus achieving a more accurate classification of mam-
mography images benign and malignant. The method proposed in
this paper not only improves the benign and malignant classifi-
cation performance of mammography images, but also provides
doctors with more objective and accurate diagnosis results, which
has important clinical application value and research significance.
Acknowledgments
The paper supported by The General Object of National Natural
Science Foundation (61772358) Research on the key technology of
BDS precision positioning in complex landform; Project supported
by International Cooperation Project of Shanxi Province (Grant No.
201603D421014) Three-Dimensional Reconstruction Research of
Quantitative Multiple Sclerosis Demyelination; International Coop-
eration Project of Shanxi Province (Grant No. 201603D421012):
Research on the key technology of GNSS area strengthen informa-
tion extraction based on crowd sensing.
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