Classification of Breast Ultrasound Images: An Analysis Using Machine Intelligent Based Approach

Abstract

Purpose: Breast Cancer (BC) is considered as one of the most dangerous diseases, especially in women. The survivability of the patient is a challenging task if the breast cancer is in severe stage. It is very much essential for the early classification of breast ultrasound images (BUIs) into several categories such as benign (BN), malignant (MG) and normal (NL), etc. so that preventive measures can be taken accordingly at the earliest. 

Approach: In this work, a machine intelligent (MI) based approach is proposed for the classification of BUIs into the BN, MG and NL types. The proposed approach is focused on the stacking (hybridization) of Logistic Regression (LRG), Support Vector Machine (SVMN), Random Forest (RFS) and Neural Network (NNT) methods to carry out such classification. The proposed method is compared with other machine learning (ML) based methods such as LRG, SVMN, RFS, NNT, Decision Tree (DTR), AdaBoost (ADB), Naïve Bayes (NBY), K-Nearest Neighbor (KNNH) and Stochastic Gradient Descent (SGDC) for performance analysis.

Result: The proposed method and other ML based methods have been implemented using Python based Orange 3.26.0. In this work, 750 TLDIs having 250 numbers of each type such as BN, MG and NL are taken from the Kaggle source. The performance of all the methods is assessed using the performance parameters such as classification accuracy (CA), F1, Precision (PR) and Recall (RC). From the results, it is found that the proposed method is capable of providing better classification results in terms of CA, F1, PR and RC as compared to other ML based methods such as LRG, SVMN, RFS, NNT, DTR, ADB, NBY, KNNH and SGD.   

Originality: In this work, a MI based approach is proposed by focusing on the stacking of LRG, SVMN, RFS and NNT methods to carry out the classification of BUIs into several types such as BN, MG and NL. The proposed approach performs better in terms of CA, F1, PR and RC as compared to LRG, SVMN, RFS, NNT, DTR, ADB, NBY, KNNH and SGDC methods.  

Paper Type: Conceptual Research.