Detecting Breast Cancer using Neural Networks

Eastern-European Journal of Enterprise Technologies

Breast cancer is one of the most common kinds of cancers that infect females in the whole world. It has happened when the cells in breast tissues start to grow in an uncontrollable way. Because it leads to death, early detection and diagnosis is a very important task to save the patient's life. Due to the restriction of human observers, computer plays a significant role in detecting early cancer signs. The proposed system uses a multi-resolution analysis and a top-hat operation for detecting the suspicious regions in a mammogram image. The discrete wavelet transform feature analysis is utilized for extracting features from the region of interest. Fuzzy Logic (FL) and Probabilistic Neural Network (PNN) are utilized for classifying the tumor into normal or abnormal. The differences between the proposed system and other researches are the use of adaptive threshold value depending on each image, by using Discrete Wavelet Transform (DWT) in both segmentation and feature extraction phases, which decrease complexity and time. Additionally, the detection of more than one tumor in the breast mammogram image and the utilization of FL and PNN work on increasing the system efficiency that led to raising the accuracy rate of the system and reducing the time. The obtained results of accuracy, sensitivity, and specificity were equal to 99 %, 98 %, and 47 %, respectively, and these results showed that the proposed system is more accurate than the other previous related works

Expert Systems with Applications, 2005

The high incidence of breast cancer has increased significantly in the recent years. The most familiar breast tumors types are mass and microcalcifications (Mcs). Mammogram is considered the most reliable method in early detection of breast cancer. Computer-aided diagnosis system can be very helpful for radiologist in detection and diagnosing abnormalities earlier and faster than traditional screening programs. Several techniques can be used to accomplish this task. In this work, the authors present a preprocessing method, based on homomorphic filtering and wavelet, to extract the abnormal Mcs in mammographic images. The authors use four different methods of feature extraction for classification of normal and abnormal patterns in mammogram. Four different feature extraction methods are used here are Wavelet, Gist, Gabor and Tamura. A classification system based on neural network and nearest neighbor classification is used.

This paper presents a computer aided diagnosis (CAD) system for automatic classification of breast masses in digital mammograms. Initially, Digital mammogram is pre-processed by 2D-median filter, connected component labelling method, and morphological functions for breast extraction. Wavelet transform is used for enhancement of mammogram and triangular mask is used for pectoral muscle suppression. Morphological functions like opening, closing, erosion, dilation and reconstruction are used for the segmentation of mammogram to extract region of interest (ROI). From ROI, intensity histogram based texture features are extracted. Extracted features are fed into classifier algorithm. In this proposed work, concept of neural network is used for classification, which is applied for two levels. In the first level, neural network classify the segmented ROI into normal (without tumor) and abnormal (with tumor) ROI. Second level neural network classify abnormal ROI into malignant and benign masses. The proposed CAD system achieves 96.07% specificity and 94.73% sensitivity at first level classification, 91.66% specificity and 80% sensitivity at second level.

Proceedings of the EURO-SIAM: European Conference for the Applied Mathematics and Informatics, Athens, Greece., 2010

Artificial Neural Network (ANN) techniques are increasingly being applied in many areas of medical fields for the analysis of complex data. In this paper, a new morphological approach based ANN is developed for an efficient detection of breast cancer from mammograms. The developed scheme consists of three steps: 1) a preprocessing step using traditional image enhancement techniques, 2) a proposed morphological segmentation algorithm and, 3) ANN using some extracted features is being applied for possible detection of abnormal cancer cells. The feasibility of the proposed approach was explored on 32 commonly virulent images provided by the Medical City Hospital in Jordan. The obtained results are encouraging; performing well in segmenting breast medical images and often generates a favorable detection of breast cancer from mammograms.

Indian Journal of Science and Technology, 2012

In this paper, we propose a complementary technique of breast c a nc er diagnosis that covers five stages of breast cancer detection based on mammography, which solves many of the problems found otherwise. We also show a very large area w h e r e many methods and techniques can be successfully merged in order to obtain a useful result for human use. These include scaling of the image, removing small objects, smoothing, extracting features, ROI extraction and many image processing techniques. Besides, neural networks are used here to train the system to detect cancer according to the dataset. This combination of multiple techniques can solve problems of the breast cancer detection with a high degree of accuracy. Examples and comparisons are given to illustrate and prove this method.

2012

Mammograms can be used to check for breast cancer in women. In this paper, we have proposed breast cancer detection into two stages. In the first stage, mammograms have to classify into malignant and benign. While in second stage, the type of abnormality is detected. Features have been extracted using Discrete Wavelet Transform. These wavelet based features has been reduced using Principle Component Analysis. Those images which have been classified as malignant in the first stage are further classified into six classes to check its abnormality. It has been observed that the accuracy of classification of abnormalities is more than 90%. Mammographic Institute Society Analysis dataset is used for experimentation.

2020

Breast tumor is the main cause for death amongst women. The aim of this project is plan and contrivance a MATLAB created image processing structure to extract features of breast cancer images in order to classify breast cancer through neural network from mammogram x-rays image (MXI). Breast Cancer (BC) happens several of the most common reasons of mortality including women international. Therefore, this development arranges the organization in preparing the finding of the disease computerized so that further and more citizens may obtain it diagnosed in the early hours so as become treated. Therefore, the experimental finding supports to protect the natural life of the ladies. Brest imaging is the elementary diagnosis for chest disease. It contain several articles that unhelpfully effects in finding of the breast tumour. The indications of recognition exist areas and small scale organization bands that are essential in quick exposure of breast tumour. This system has also the feature...

Breast cancer is very common and is considered as the second dangerous disease all over the world due to its death rate. Affected can survive if the disease diagnoses before the appearance of major physical changes in the body. Now a day, mammographic (X-ray of breast region) images are widely used for premature revealing of breast cancer. Aim of the proposed system is to design a Computer Aided Diagnosis system (CAD) used to distinguish between benign (non-cancerous) and malignant (cancerous) mammogram. CAD system are used to help radiologist to increase his diagnosis accuracy. In the proposed system, texture features from mammogram were calculated using Gray Level Co-occurrence Matrix (GLCM) along 0°, from the calculate features most effective features having large contribution to achieve the desired output were chosen and applied to Artificial Neural Network (ANN) for training and classification, as ANN is widely use in various field such as, pattern recognition, medical diagnosis, machine learning and so on. For this research work mini-MIAS database is used and the overall sensitivity, specificity and accuracy achieved by using the proposed system is 99.3%, 100% and 99.4% respectively.

Journal of Computer Science, 2007

Breast cancer accounts for the second most cancer diagnoses among women and the second most cancer deaths in the world. In fact, more than 11000 women die each year, all over the world, because this disease. The automatic breast cancer diagnosis is a very important purpose of medical informatics researches. Some researches has been oriented to make automatic the diagnosis at the step of mammographic diagnosis, some others treated the problem at the step of cytological diagnosis. In this work, we describes the current state of the ongoing the BC automated diagnosis research program. It is a software system that provides expert diagnosis of breast cancer based on three step of cytological image analysis. The first step is based on segmentation using an active contour for cell tracking and isolating of the nucleus in the studied image. Then from this nucleus, have been extracted some textural features using the wavelet transforms to characterize image using its texture, so that malign texture can be differentiated from benign on the assumption that tumoral texture is different from the texture of other kinds of tissues. Finally, the obtained features will be introduced as the input vector of a Multi-Layer Perceptron (MLP), to classify the images into malign and benign ones.

2000

Breast Cancer is one of the significant reasons for death among ladies. Many research has been done on the diagnosis and detection of breast cancer using various image processing and classification techniques. Nonetheless, the disease remains as one of the deadliest disease. Having conceive one out of six women in her lifetime. Since the cause of breast cancer stays obscure, prevention becomes impossible. Thus, early detection of tumour in breast is the only way to cure breast cancer. Using CAD (Computer Aided Diagnosis) on mammographic image is the most efficient and easiest way to diagnosis for breast cancer. Accurate discovery can effectively reduce the mortality rate brought about by using mamma cancer. Masses and microcalcifications clusters are an important early symptoms of possible breast cancers. They can help predict breast cancer at it's infant state. The image for this work is being used from the DDSM Database (Digital Database for Screening Mammography) which contains approximately 3000 cases and is being used worldwide for cancer research. This paper quantitatively depicts the analysis methods used for texture features for detection of cancer. These texture featuresare extracted from the ROI of the mammogram to characterize the microcalcifications into harmless, ordinary or threatening. These features are further decreased using Principle Component Analysis(PCA) for better identification of Masses. These features are further compared and passed through Back Propagation algorithm (Neural Network) for better understanding of the cancer pattern in the mammography image.