<p>An automated mammogram classification system using modified support vector machine</p>

Abstract

Purpose Breast cancer remains a serious public health problem that results in the loss of lives among women. However, early detection of its signs increases treatment options and the likelihood of cure. Although mammography has been established to be a proven technique of examining symptoms of cancer in mammograms, the manual observation by radiologists is demanding and often prone to diagnostic errors. Therefore, computer aided diagnosis (CADx) systems could be a viable alternative that could facilitate and ease cancer diagnosis process; hence this study. Methodology The inputs to the proposed model are raw mammograms downloaded from the Mammographic Image Analysis Society database. Prior to the classification, the raw mammograms were preprocessed. Then, gray level co-occurrence matrix was used to extract fifteen textural features from the mammograms at four different angular directions: θ={0°, 45°, 90°, 135°}, and two distances: D={1,2}. Afterwards, a two-stage support vector machine was used to classify the mammograms as normal, benign and malignant. Results All of the 37 normal images used as test data were classified as normal (no false positive) and all 41 abnormal images were correctly classified to be abnormal (no false negative), meaning that the sensitivity and specificity of the model in detecting abnormality is 100%. After the detection of abnormality, the system further classified the abnormality on the mammograms to be either “benign” or “malignant”. Out of 23 benign images, 21 were truly classified as benign. Also, out of 18 malignant images, 17 were truly classified to be malignant. From these findings, the sensitivity, specificity, positive predictive value, and negative predictive value of the system are 94.4%, 91.3%, 89.5%, and 95.5%, respectively. Conclusion This article has further affirmed the prowess of automated CADx systems as a viable tool that could facilitate breast cancer diagnosis by radiologists.

Mammogram Learning System for Breast Cancer Diagnosis Using Deep Learning SVM

“…Simple morphological operations are employed to remove the information embedded in the X-ray image. The previous studies in [2,4,9,11,12], Contrast Limited Adaptive Histogram Equalization (CLAHE), are chosen as a contrast enhancement technique. The over enhancement by AHE is removed by CLAHE as the enhancement is applied on a local intensity histogram.…”

“…The advances in computing technology laid the fundamental methodology for medical image analysis in the medical domain, emphasizing cancer classification. There are two dominant designs; the class prediction model [2][3][4][5][6][7][8][9][10][11][12][13] and the class discovery model [11,14]. Each design is used as a different study's objective.…”

“…Different supervised learning algorithms are developed and used in breast cancer diagnoses such as k-nearest neighbour [2], naive Bayes [3], support vector machine [4,[5][6][7], Linear regression [8], artificial neural network [9][10], decision tree [11][12] and extreme learning machine [13]. These algorithms require a separate feature extraction module to extract dominant features from the training samples.…”

“…These algorithms require a separate feature extraction module to extract dominant features from the training samples. Features from time-domain; Shape [5][6][7][8], texture [4][5][6]8], Gray level [5] and moment invariants [9][10] and frequency domain; Wavelet [3,13], Bandlet and Ripplet [2], Hough transform [6], and Surfacelet transform [12] are employed. Feature reduction techniques such as principal component analysis [11,13] and the Fisher score [9] are also employed to reduce the feature space if needed.…”

Development of a Web Based Intelligent Dietetic System

“…Expert systems eras are advancing daily due to the efficient usage options it provides and the reliability of its applications (Li et al, 2002). The need for medical science and artificial intelligence (AI) automation to combine cannot be over emphasized, but they are almost beginning to overlap upon each other and in certain situations (Kayode et al, 2019, Oladele et al 2019. As humans across the planet are becoming increasingly cognizant of their weight, eating progressively sound regimens and staying away from low quality nourishment is essential for healthy living, a framework that can quantify calories and sustenance in our ordinary suppers will be extremely helpful for keeping up our wellbeing .…”