Classification of Benign and Malignant Breast Cancer using Supervised Machine Learning Algorithms Based on Image and Numeric Datasets

Abstract

Breast cancer has been identified as the second leading cause of death among women worldwide after lung cancer and hence, it becomes extremely crucial to identify it at an early stage, which can considerably increase the chances of survival. The most important part in cancer detection is to be able to differentiate between benign and malignant tumors and this is where the work of Machine Learning comes in. Taking all the dependent features upon consideration, Supervised Machine Learning methods allow for classification with higher degree of accuracy and improve upon the misdiagnosis of the physicians, which might occur almost 20% of the time. In our paper, we are focusing towards understanding the shortcomings of digital mammograms in detection of breast cancer and utilize Machine Learning classifiers for the classification of benign and malignant tumors using image analysis. Apart from this, we are also looking into implementing Supervised Machine Learning classifiers such as Decision Tree, K Nearest Neighbour (KNN), Random Forest and Gaussian Naive Bayes classifiers for assessing the risks involved with breast cancer by analyzing the biomarkers that are involved with it. Our aim is to provide a comprehensive view on prediction of breast cancer through Machine Learning through both image and data analyses, which can play a pivotal role in prevention of misdiagnosis in future. Fig. 1. gives a layout for the breast cancer prediction using Supervised Machine learning classifiers.

Comparative Analysis of Data Mining Algorithms for Cancer Gene Expression Data

“…Ray et al [20] explored different classification methods (Gaussian NB, kNN, DT, RF) for detecting breast cancer involving both numeric and image datasets. It was found that the accuracy of RF was better in both numeric and image datasets.…”

Predicting Invasive Ductal Carcinoma Tissues in Whole Slide Images of Breast Cancer by Using Convolutional Neural Network Model and Multiple Classifiers in Google Colab

“…The major types of BC surgeries are Lumpectomy, Mastectomy, Sentinel Node Biopsy, Axillary Lymph Node Dissection and Contralateral Prophylactic Mastectomy [4]. The common types of BC are DCIS (Ductal Carcinoma In-Situ), LCIS (Lobular Carcinoma In-Situ), Invasive Ductal Carcinoma(IDC) and ILC (Invasive Lobular Carcinoma) [5]. The WSI (Whole Slide Imaging) are mainly utilized in the eld of clinical education and digital image analysis.…”

Outperforming Clinical Practices in Breast Cancer Detection: A Superior Dense Neural Network in Classification and False Negative Reduction

“…In comparison to these clinical BC detection practices, Machine Learning (ML) has been proven as a promising tool for classifying benign and malignant tumors providing higher accuracy than physicians. At the same time, these techniques decrease the rate of misdiagnosis, which can account for up to almost 20% of diagnostic cases [12]. Nonetheless, misclassification is still a threat leading to harmful consequences for patients.…”

“…On the other hand, FP classification is associated with severe negative physical and mental consequences for the misdiagnosed patients [13]. As a result of these misclassifications, ML models require special attention to class imbalance by applying various approaches such as adequate sampling strategies, evolution metrics, or classification algorithms [12]- [15].…”