Implementing Multilabeling, ADASYN, and ReliefF Techniques for Classification of Breast Cancer Diagnostic through Machine Learning: Efficient Computer-Aided Diagnostic System.

Taha Muthar Khan,Shengjun Xu,Zullatun Gull Khan,Saverio Maietta,Muhammad Uzair Chishti

doi:10.1155/2021/5577636

Abstract

Multilabel recognition of morphological images and detection of cancerous areas are difficult to locate in the scenario of the image redundancy and less resolution. Cancerous tissues are incredibly tiny in various scenarios. Therefore, for automatic classification, the characteristics of cancer patches in the X-ray image are of critical importance. Due to the slight variation between the textures, using just one feature or using a few features contributes to inaccurate classification outcomes. The present study focuses on five different algorithms for extracting features that can extract further different features. The algorithms are GLCM, LBGLCM, LBP, GLRLM, and SFTA from 8 image groups, and then, the extracted feature spaces are combined. The dataset used for classification is most probably imbalanced. Additionally, another focal point is to eradicate the unbalanced data problem by creating more samples using the ADASYN algorithm so that the error rate is minimized and the accuracy is increased. By using the ReliefF algorithm, it skips less contributing features that relieve the burden on the process. Finally, the feedforward neural network is used for the classification of data. The proposed method showed 99.5% micro, 99.5% macro, 0.5% misclassification, 99.5% recall rats, specificity 99.4%, precision 99.5%, and accuracy 99.5%, showing its robustness in these results. To assess the feasibility of the new system, the INbreast database was used.

Highlights

Breast cancer is considered a key health issue in women which is causing a high rate of casualty. e initial diagnosis of breast cancer with mammographic screening and appropriate pharmacological treatments has steadily increased the prognosis of breast cancer [1]. ese include mammography, biopsy, ultrasound image, and thermography [2].e biopsy is painful procedure and rather expensive
Adaptive Synthetic (ADASYN) bases its operation on weighting the examples of the minority classes according to their difficulty of being learned; more synthetic data will be generated from the more difficult samples, and fewer samples in the case of the easier to learn [12]. is sampling method aims to help the classifier in two ways: first, reducing the error produced by the imbalance of the classes and focusing the synthetic samples only on the difficult samples to learn [45, 46]
E archive is accessible on this web page. http://medicalresearch.inescporto.pt/breastresearch/ind ex.php/Get_INbreast_Database

Summary

Introduction

Breast cancer is considered a key health issue in women which is causing a high rate of casualty. e initial diagnosis of breast cancer with mammographic screening and appropriate pharmacological treatments has steadily increased the prognosis of breast cancer [1]. ese include mammography, biopsy, ultrasound image, and thermography [2].e biopsy is painful procedure and rather expensive. Today’s world image recognition methods have an important role to play in the analysis of tumor images by using a machine learning methodology. It uses a random generator, a function extractor, and a classifier to model a doctor’s enquiry and construct a personalized questionnaire [6]. Microwavebased imaging techniques were developed for breast cancer detection [7]. Many classification methods are used in the algorithms of machine learning like decision tree (C45), support vector machine (SVM), and naive Bayes algorithm [8]. A fuzzy support vector machine (FSVM) was implemented by Nedeljkovic to define and characterize the amount of breast ultrasound [10]. In order to eradicate such errors, algorithms were developed to assist radiologists. erefore, this distinctive attribute of the tissue patches in the image played an important role in classification

Methods

Results

Conclusion