Breast Cancer: Modelling and Detection

This paper reviews a number of the mathematical models used in cancer modelling and then chooses a specific cancer, breast carcinoma, to illustrate how the modelling can be used in aiding detection. We then discuss mathematical models that underpin mammographic image analysis, which complements models of tumour growth and facilitates diagnosis and treatment of cancer. Mammographic images are notoriously difficult to interpret, and we give an overview of the primary image enhancement technologies that have been introduced, before focusing on a more detailed description of some of our own recent work on the use of physics-based modelling in mammography. This theoretical approach to image analysis yields a wealth of information that could be incorporated into the mathematical models, and we conclude by describing how current mathematical models might be enhanced by use of this information, and how these models in turn will help to meet some of the major challenges in cancer detection

Enhanced Artificial Intelligence System for Diagnosing and Predicting Breast Cancer Using Deep Learning

Breast cancer is the leading cause of death among women with cancer. Computer-aided diagnosis is an efficient method for assisting medical experts in early diagnosis, improving the chance of recovery. Employing artificial intelligence (AI) in the medical area is very crucial due to the sensitivity of this field. This means that the low accuracy of the classification methods used for cancer detection is a critical issue. This problem is accentuated when it comes to blurry mammogram images. In this paper, convolutional neural networks (CNNs) are employed to present the traditional convolutional neural network (TCNN) and supported convolutional neural network (SCNN) approaches. The TCNN and SCNN approaches contribute by overcoming the shift and scaling problems included in blurry mammogram images. In addition, the flipped rotation-based approach (FRbA) is proposed to enhance the accuracy of the prediction process (classification of the type of cancerous mass) by taking into account the different directions of the cancerous mass to extract effective features to form the map of the tumour. The proposed approaches are implemented on the MIAS medical dataset using 200 mammogram breast images. Compared to similar approaches based on KNN and RF, the proposed approaches show better performance in terms of accuracy, sensitivity, spasticity, precision, recall, time of performance, and quality of image metrics

A Computational Approach to Predict the Severity of Breast Cancer through Machine Learning Algorithms

Breast cancer is one type of cancer which causes from breast tissue. A lump in the breast, skin dimpling, breast shape changes, fluid from the nipple, or a red scaly patch of skin are some of signs of breast cancer. In the world, cancer is one of the most leading causes of deaths among the women. Among the cancer diseases, breast cancer is especially a concern in women. Mammography is one of the methods for finding tumor in the breast. This method is utilized to detect the cancer which is helpful for the doctor or radiologists. Due to the inexperience�s in the field of cancer detection, the abnormality is missed by doctor or Radiologists. Segmentation is very expensive for doctor and radiologists to examine the data in the mammogram. In mammogram the accuracy rate is based on the image segmentation. The recent clustering techniques are presented in this paper for detection of breast cancer. These Classification algorithms have been mostly studied which is applied in a various application areas. To maximize the efficiency of the searching process various clustering techniques are recommended. In this paper, we have presented a survey of Classification techniques

Hybrid Image Mining Methods to Classify the Abnormality in Complete Field Image Mammograms Based on Normal Regions

Breast Cancer now becomes a common disease among woman in developing as well as developed countries. Many noninvasive methodologies have been used to detect breast cancer. Computer Aided diagnosis through, Mammography is a widely used as a screening tool and is the gold standard for the early detection of breast cancer. The classification of breast masses into the benign and malignant categories is an important problem in the area of computer-aided diagnosis of breast cancer. We present a new method for complete total image of mammogram analysis. A mammogram is analyzed region by region and is classified as normal or abnormal. We present a hybrid technique for extracting features that can be used to distinguish normal and abnormal regions of a mammogram. We describe our classifier technique that uses a unique re-classification method to boost the classification performance. Our proposed hybrid technique comprises decision tree followed by association rule miner shows most proficient and promising performance with high classification rate compared to many other classifiers. We have tested this technique on a set of ground-truth complete total image of mammograms and the result was quite effective

Detection of Malignant Tumour in Mammography Images Using Artificial Neural Networks with Fuzzy Rules

Breast cancer is a collection of cancer cells that starts in the breast cells and it expands from tissue of breast. Now a day Mammogram is one technique to detect the breast cancer earlyusing x-ray image of breast and it is used to reduce the deaths of breast cancer. This breast cancer disease is curable if discovered starting stage. This paper studies different methods utilized for the detection of breast cancer using mammogram classification. In this paper, the feature extraction and classification of mammogram image can be done by the artificial neural networks. Different kinds of feature extraction from mammogram image to detecting the bread cancer contains shape, position and surface features etc., this image feature extraction is significant in classification of image. By utilizing the image processing these image features are extracted. Image segmentation is performed for feature extraction of mammogram image, in this process image is partitioned into multiple segments, therefore when change the image representation into something that is more significant and simple to examine. Here the fuzzy rules are introduced to process the related data from cases of breast cancer in mammogram image in order to give the risk diagnosis of breast cancer. The preprocessing method is used to sustain an effectiveness of image by correct and adjusting the mammogram image and also it is used to improve the image quality and create it ready for additional working by reducing the unrelated noise to provide new brightness value in output image it is called as filtration and unwanted parts of background of mammogram image is eliminated. Some techniques are discussed for mammogram image classification to earlier detection of breast cancer

An Enhancement of Multi Classifiers Voting Method for Mammogram Image Based on Image Histogram Equalization

Breast cancer is one the most curable cancer types if it can be diagnosed early. Research efforts have reported with increasing confirmation that the computation methods have greater accurate diagnosis ability. An enhancement of multi classifiers voting technique based on histogram equalization as a preprocessing stage proposed in this paper. The methodology is based on five phases starting by mammogram images collection, preprocessing (histogram equalization and image cropping based region of interest (ROI)), features extracting, classification and last evaluating the classification results. An experimental conducted on different training-testing partitions of the dataset. The numerical results demonstrate that the proposed scheme achieves an accuracy rate of 81.25% and outperformed the accuracy of voting method without using histogram equalization

Classification of Mammogram Images by Using SVM and KNN

Breast cancer is a fairly diverse illness that affects a large percentage of women in the west. A mammogram is an X-ray-based evaluation of a woman's breasts to see if she has cancer. One of the earliest prescreening diagnostic procedures for breast cancer is mammography. It is well known that breast cancer recovery rates are significantly increased by early identification. Mammogram analysis is typically delegated to skilled radiologists at medical facilities. Human mistake, however, is always a possibility. Fatigue of the observer can commonly lead to errors, resulting in intraobserver and interobserver variances. The image quality affects the sensitivity of mammographic screening as well. The goal of developing automated techniques for detection and grading of breast cancer images is to reduce various types of variability and standardize diagnostic procedures. The classification of breast cancer images into benign (tumor increasing, but not harmful) and malignant (cannot be managed, it causes death) classes using a two-way classification algorithm is shown in this study. The two-way classification data mining algorithms are utilized because there are not many abnormal mammograms. The first classification algorithm, k-means, divides a given dataset into a predetermined number of clusters. Support Vector Machine (SVM), a second classification algorithm, is used to identify the optimal classification function to separate members of the two classes in the training dat

Breast cancer diagnosis: a survey of pre-processing, segmentation, feature extraction and classification

Machine learning methods have been an interesting method in the field of medical for many years, and they have achieved successful results in various fields of medical science. This paper examines the effects of using machine learning algorithms in the diagnosis and classification of breast cancer from mammography imaging data. Cancer diagnosis is the identification of images as cancer or non-cancer, and this involves image preprocessing, feature extraction, classification, and performance analysis. This article studied 93 different references mentioned in the previous years in the field of processing and tries to find an effective way to diagnose and classify breast cancer. Based on the results of this research, it can be concluded that most of today’s successful methods focus on the use of deep learning methods. Finding a new method requires an overview of existing methods in the field of deep learning methods in order to make a comparison and case study