An enhancement of mammogram images for breast cancer classification using artificial neural networks

Abstract

<p><span id="docs-internal-guid-12eaaa5f-7fff-c428-95bf-97a7381b2976"><span>Breast cancer is the most driving reason for death in women in both developed and developing nations. For the plan of effective classification of a system, the selection of features method must be used to decrease irregularity part in mammogram images. The proposed approach is used to crop the region of interests (ROIs) manually. Based on that number of features are extracted. In this proposed method a novel hybrid optimum feature selection (HOFS) method is used to find out the significant features to reach maximum accuracy for this classification. A number of selected features is applied to train the neural network. In this proposed method accessible informational index from the mini–mammographic image analysis society (MIAS) database was used. The classification of this mammogram database involved a neural networks classifier which attained an accuracy of 99.7% with a sensitivity of 99.5%, and specificity of 100% as the area under the curve (AUC) is 0.9975 and matthew’s correlation coefficient (MCC) represents a binary class value which reached the value of 0.9931. It can be useful in a computer-aided diagnosis system (CAD) framework to help the radiologist in analyzing breast cancer. Results achieved with the proposed method are better compared to recent work.</span></span></p>

Exploring the performance of feature selection method using breast cancer dataset

“…Accuracy is the most widely employed performance measure for validating the predictive performance of classification model [23]- [25]. Hence, in this study we have employed the predictive accuracy to evlaute the performance of the developed model.…”

Brain stroke computed tomography images analysis using image processing: A Review

“…Every patient might have different observed data, and the interpretation of the data depends on the experience of those skilled in the art, this can lead to errors within and between observers [77]. Segmentation ensues by dividing digital images into multiple segments into nonoverlapped areas that share characteristics such as shape, intensity, or texture to locate and identify objects and boundaries in an image [18], [78]- [89]. In further parts of this paper, the various techniques of segmentation are discussed and compared.…”

“…This problem amplifies the notion as "time is brain". Despite a critical need, there is no established automatic system for stroke, although several automated systems in other fields, including mammography and chest, are among others [15]- [18]. Furthermore, based on studies on computed-aided diagnosis (CAD) systems and technology, it shows that the diagnostic accuracy of radiologists can be improved by using CAD [19]- [29].…”

Dengue classification method using support vector machines and cross-validation techniques

“…The data used in the expert system can vary, such as images [2]- [4], signals [5], or medical record data which includes name, age, laboratory test results, and symptoms of the patient [6]- [8]. In the medical field, several expert systems have been developed, for example for estimating drug doses [6], [9], monitoring the disease progress [1], [2], [10], and detecting several types of diseases such as diabetes mellitus [11], pancreatic cancer [12], breast cancer [13], glaucoma [14], and dengue fever (DF) [15]- [17]. Dengue fever is an arboviral disease caused by infection with one of the four dengue virus serotypes.…”

“…It can be applied using several methods, such as rule-based [21]- [24], or using machine learning [25]. The following methods were used in prior studies to implement the machine learning-based classification process: naive Bayes [6], logistic regression [12], random forest (RF) [8], [12], [16], k-nearest neighbor (KNN) [26], artificial neural network (ANN) [10], [13], dan support vector machine (SVM) [8], [14].…”