Breast cancer recognition based on performance evaluation of machine learning algorithms

Abstract

Breast cancer is the one common cause of death in both developed worlds and the most death-causing disease diagnosed among women. <span lang="EN-US">Early recognition of this condition can help to minimize death rates. The breast problem statement, in brief, is not reliable for accuracy recognition. They have a high degree of classification accuracy as well as diagnostic capabilities. The most common classifications are normal, benign cancer, and malignant cancer. Machine learning (ML) techniques are now widely used in the classification of breast cancer. In this paper, some machine learning technics have been investigated to diagnose breast cancer (BC) on magnetic resonance imaging (MRI) images using multi-step processes. The first step has been to take the MRI image as an input image and have been pre-processing an image, then use feature extraction by using (scale-invariant feature transform (SIFT), histogram of oriented gradient (HOG), local binary patterns (LBP), bag of words (BoW), and edge-oriented histogram (EOH)). Next step we implement the classifying algorithms (KNN, decision tree (DT), naïve Bayes, ANN, SVM, RF, AdaBoost), have been used to detect and classify the normal or breast cancer region for this purpose datasets like ACRIN-Contralateral-Breast-MRI, In and breast cancer MRI dataset) has been collected our breast cancer MRI images from Erbil and Sulaymaniyah hospital the results was 91.9%, the result of ACRIN was 97% and the results Breast Cancer was 92.3%.</span>

Breast cancer diagnosis based on support vector machine techniques

“…In the HOG descriptor, the ROI is partitioned into tiny, related sections called cells, and produced the histogram of gradients for every cell rather than the complete ROI. The gray scale value is calculated for blocks, which are bigger overlapping sections [22]. These blocks are created by cells that have gathered in ROI.…”

Detection of chronic kidney disease using binary whale optimization algorithm

“…Machine learning (ML) has been a potent tool in healthcare in recent years. Examples include breast cancer recognition [3], detection of lung cancer [4], parkinson disease classification [5], diagnosis of hepatitis disease [6], prediction of cardiac illness [7], chronic and infectious diseases [8], the severity grading and identifying of diabetic retinopathy [9], [10], and the prediction of infected COVID-19 [11]- [14]. The accuracy of diagnosis can be significantly increased by using ML in the early identification of CKD.…”