Detection of cancer tumors in mammography images using support vector machine and mixed gravitational search algorithm
09 Mar 2016-pp 98-101
TL;DR: The experimental results show that the proposed MGSA-SVM method is able to optimize both feature selection and the SVM parameters for the breast cancer tumor detection.
Abstract: In this paper, support vector machine (SVM) and mixed gravitational search algorithm (MGSA) are utilized to detect the breast cancer tumors in mammography images. Sech template matching method is used to segment images and extract the regions of interest (ROIs). Gray-level co-occurrence matrix (GLCM) is used to extract features. The mixed GSA is used for optimization of the classifier parameters and selecting salient features. The main goal of using MGSA-SVM is to decrease the number of features and to improve the SVM classification accuracy. Finally, the selected features and the tuned SVM classifier are used for detecting tumors. The experimental results show that the proposed method is able to optimize both feature selection and the SVM parameters for the breast cancer tumor detection.
Citations
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TL;DR: This work has put a special emphasis on the Convolutional Neural Network (CNN) method for breast image classification, and described the involvement of the conventional Neural Network, Logic Based classifiers such as the Random Forest algorithm, Support Vector Machines (SVM), Bayesian methods, and a few of the semisupervised and unsupervised methods.
Abstract: Breast cancer is one of the largest causes of women's death in the world today. Advance engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. The involvement of digital image classification allows the doctor and the physicians a second opinion, and it saves the doctors' and physicians' time. Despite the various publications on breast image classification, very few review papers are available which provide a detailed description of breast cancer image classification techniques, feature extraction and selection procedures, classification measuring parameterizations, and image classification findings. We have put a special emphasis on the Convolutional Neural Network (CNN) method for breast image classification. Along with the CNN method we have also described the involvement of the conventional Neural Network (NN), Logic Based classifiers such as the Random Forest (RF) algorithm, Support Vector Machines (SVM), Bayesian methods, and a few of the semisupervised and unsupervised methods which have been used for breast image classification.
94 citations
Cites methods from "Detection of cancer tumors in mammo..."
...Shirazi and Rashedi [123] (1) GLCM Ultrasound 322 (1) ROI extracted for reducing redundant complexity....
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TL;DR: This paper has classified a set of Histopathological Breast-Cancer images utilizing a state-of-the-art CNN model containing a residual block and examined the performance of the novel CNN model as Histopathology image classifier.
Abstract: Identification of the malignancy of tissues from Histopathological images has always been an issue of concern to doctors and radiologists. This task is time-consuming, tedious and moreover very challenging. Success in finding malignancy from Histopathological images primarily depends on long-term experience, though sometimes experts disagree on their decisions. However, Computer Aided Diagnosis (CAD) techniques help the radiologist to give a second opinion that can increase the reliability of the radiologist’s decision. Among the different image analysis techniques, classification of the images has always been a challenging task. Due to the intense complexity of biomedical images, it is always very challenging to provide a reliable decision about an image. The state-of-the-art Convolutional Neural Network (CNN) technique has had great success in natural image classification. Utilizing advanced engineering techniques along with the CNN, in this paper, we have classified a set of Histopathological Breast-Cancer (BC) images utilizing a state-of-the-art CNN model containing a residual block. Conventional CNN operation takes raw images as input and extracts the global features; however, the object oriented local features also contain significant information—for example, the Local Binary Pattern (LBP) represents the effective textural information, Histogram represent the pixel strength distribution, Contourlet Transform (CT) gives much detailed information about the smoothness about the edges, and Discrete Fourier Transform (DFT) derives frequency-domain information from the image. Utilizing these advantages, along with our proposed novel CNN model, we have examined the performance of the novel CNN model as Histopathological image classifier. To do so, we have introduced five cases: (a) Convolutional Neural Network Raw Image (CNN-I); (b) Convolutional Neural Network CT Histogram (CNN-CH); (c) Convolutional Neural Network CT LBP (CNN-CL); (d) Convolutional Neural Network Discrete Fourier Transform (CNN-DF); (e) Convolutional Neural Network Discrete Cosine Transform (CNN-DC). We have performed our experiments on the BreakHis image dataset. The best performance is achieved when we utilize the CNN-CH model on a 200× dataset that provides Accuracy, Sensitivity, False Positive Rate, False Negative Rate, Recall Value, Precision and F-measure of 92.19%, 94.94%, 5.07%, 1.70%, 98.20%, 98.00% and 98.00%, respectively.
91 citations
Cites methods from "Detection of cancer tumors in mammo..."
...[6], where Regions of Interest (ROI) have been extracted for reduction of the computational complexity....
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TL;DR: This paper includes a new approach, applied on the Mini-MIAS dataset of 322 images, involving a pre-processing method and inbuilt feature extraction using K-means clustering for Speed-Up Robust Features (SURF) selection, demonstrating that the accuracy rate of the proposed automated DL method usingK-mean clustering with MSVM is improved as compared with a decision tree model.
72 citations
01 Sep 2016
TL;DR: It is observed that the proposed scheme with 3NN classifier outperforms SVM and ANN by giving 95% accuracy, 100% sensitivity and 90% specificity to classify mammogram images as normal or abnormal.
Abstract: Breast cancer is one of the most common forms of cancer in women worldwide. Most cases of breast cancer can be prevented through screening programs aimed at detecting abnormal tissue. So, early detection and diagnosis is the best way to cure breast cancer to decrease the mortality rate. Computer Aided Diagnosis (CAD) system provides an alternative tool to the radiologist for the screening and diagnosis of breast cancer. In this paper, an automated CAD system is proposed to classify the breast tissues as normal or abnormal. Artifacts are removed using ROI extraction process and noise has been removed by the 2D median filter. Contrast-Limited Adaptive Histogram Equalization (CLAHE) algorithm is used to improve the appearance of the image. The texture features are extracted using Gray Level Co-occurrence Matrix (GLCM) of the region of interest (ROI) of a mammogram. The standard Mammographic Image Analysis Society (MIAS) database images are considered for the evaluation. K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Artificial Neural Network (ANN) are used as classifiers. For each classifier, the performance factor such as sensitivity, specificity and accuracy are computed. It is observed that the proposed scheme with 3NN classifier outperforms SVM and ANN by giving 95% accuracy, 100% sensitivity and 90% specificity to classify mammogram images as normal or abnormal.
23 citations
Cites methods from "Detection of cancer tumors in mammo..."
...in [10], presented a breast cancer detection system by combining mixed gravitational search algorithm (MGSA) and support vector machine (SVM)....
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05 Mar 2021
TL;DR: The work consists of designing a CNN model to facilitate the classification process, training the model using three different optimizers, and evaluating the model through various experiments on the BreakHis dataset.
Abstract: Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer mortality in women around the world. However, it can be controlled effectively by early diagnosis, followed by effective treatment. Clinical specialists take the advantages of computer-aided diagnosis (CAD) systems to make their diagnosis as accurate as possible. Deep learning techniques, such as the convolutional neural network (CNN), due to their classification capabilities on learned feature methods and ability of working with complex images, have been widely adopted in CAD systems. The parameters of the network, including the weights of the convolution filters and the weights of the fully connected layers, play a crucial role in the classification accuracy of any CNN model. The back-propagation technique is the most frequently used approach for training the CNN. However, this technique has some disadvantages, such as getting stuck in local minima. In this study, we propose to optimize the weights of the CNN using the genetic algorithm (GA). The work consists of designing a CNN model to facilitate the classification process, training the model using three different optimizers (mini-batch gradient descent, Adam, and GA), and evaluating the model through various experiments on the BreakHis dataset. We show that the CNN model trained through the GA performs as well as the Adam optimizer with a classification accuracy of 85%.
15 citations
References
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TL;DR: A new model for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford-Shah (1989) functional for segmentation and level sets is proposed, which can detect objects whose boundaries are not necessarily defined by the gradient.
Abstract: We propose a new model for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford-Shah (1989) functional for segmentation and level sets. Our model can detect objects whose boundaries are not necessarily defined by the gradient. We minimize an energy which can be seen as a particular case of the minimal partition problem. In the level set formulation, the problem becomes a "mean-curvature flow"-like evolving the active contour, which will stop on the desired boundary. However, the stopping term does not depend on the gradient of the image, as in the classical active contour models, but is instead related to a particular segmentation of the image. We give a numerical algorithm using finite differences. Finally, we present various experimental results and in particular some examples for which the classical snakes methods based on the gradient are not applicable. Also, the initial curve can be anywhere in the image, and interior contours are automatically detected.
10,404 citations
"Detection of cancer tumors in mammo..." refers methods in this paper
...Active contour based on curve evolution and Mumford-shah functional for segmentation was utilized in [3, 4, 5]....
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TL;DR: In this article, the authors introduce and study the most basic properties of three new variational problems which are suggested by applications to computer vision, and study their application in computer vision.
Abstract: : This reprint will introduce and study the most basic properties of three new variational problems which are suggested by applications to computer vision. In computer vision, a fundamental problem is to appropriately decompose the domain R of a function g (x,y) of two variables. This problem starts by describing the physical situation which produces images: assume that a three-dimensional world is observed by an eye or camera from some point P and that g1(rho) represents the intensity of the light in this world approaching the point sub 1 from a direction rho. If one has a lens at P focusing this light on a retina or a film-in both cases a plane domain R in which we may introduce coordinates x, y then let g(x,y) be the strength of the light signal striking R at a point with coordinates (x,y); g(x,y) is essentially the same as sub 1 (rho) -possibly after a simple transformation given by the geometry of the imaging syste. The function g(x,y) defined on the plane domain R will be called an image. What sort of function is g? The light reflected off the surfaces Si of various solid objects O sub i visible from P will strike the domain R in various open subsets R sub i. When one object O1 is partially in front of another object O2 as seen from P, but some of object O2 appears as the background to the sides of O1, then the open sets R1 and R2 will have a common boundary (the 'edge' of object O1 in the image defined on R) and one usually expects the image g(x,y) to be discontinuous along this boundary. (JHD)
5,516 citations
"Detection of cancer tumors in mammo..." refers methods in this paper
...Active contour based on curve evolution and Mumford-shah functional for segmentation was utilized in [3, 4, 5]....
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TL;DR: A new optimization algorithm based on the law of gravity and mass interactions is introduced and the obtained results confirm the high performance of the proposed method in solving various nonlinear functions.
5,501 citations
"Detection of cancer tumors in mammo..." refers methods in this paper
...Gravitational search algorithm (GSA) was proposed in [10] and in some works was used for enhancing classification algorithms [11, 12] and image processing algorithms [13]....
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TL;DR: A new model for active contours based on a geometric partial differential equation that satisfies the maximum principle and permits a rigorous mathematical analysis is proposed, which enables us to extract smooth shapes and it can be adapted to find several contours simultaneously.
Abstract: We propose a new model for active contours based on a geometric partial differential equation. Our model is intrinsec, stable (satisfies the maximum principle) and permits a rigorous mathematical analysis. It enables us to extract smooth shapes (we cannot retrieve angles) and it can be adapted to find several contours simultaneously. Moreover, as a consequence of the stability, we can design robust algorithms which can be engineed with no parameters in applications. Numerical experiments are presented.
1,948 citations
"Detection of cancer tumors in mammo..." refers methods in this paper
...Active contour based on curve evolution and Mumford-shah functional for segmentation was utilized in [3, 4, 5]....
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TL;DR: This paper investigates a new classification approach for detection of breast abnormalities in digital mammograms using Particle Swarm Optimized Wavelet Neural Network (PSOWNN) and describes the inherent discrimination capacity of the proposed system.
301 citations
"Detection of cancer tumors in mammo..." refers methods in this paper
...Some optimization techniques were used to enhance tumor detection, such as the genetic algorithm (GA) [14] and particle swarm optimization (PSO) [15]....
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