Osteosarcoma detection in histopathology images using ensemble machine learning techniques

Abstract

Osteosarcoma is a bone malignancy that severely affects the long bones of a human arm or leg. Histopathological analysis with ML (Machine Learning) algorithm offers a gainful way to examine the difference in the osteosarcoma texture. This work presents a new ensemble learning (EL) model with severity analysis in classifying bone tumor (cancer). Initially, pre-processing includes noise removal with Weighted Bilateral (WB) filtering, image quality enhancement using Parabolic Balance Contrast Enhancement (PBCE), and unwanted regions are discarded using erosion and dilation operations. The SIKC (Superpixel Improved K-means Clustering) approach characterizes the segmentation process. The extraction of valuable information is done with color and texture features, namely RGB histogram and Spatial Gray Level Dependence Matrix (Spatial GLDM). Next, the appropriate features are selected using Max_Relevance Min_Redundancy (MRMR) technique. Finally, classification is performed using EL models such as Kernel-Support Vector Machine (KSVM), Improved ANN with Beetle Swarm Optimization (IANN_BSO), and Kernel Extreme Learning Machine Optimized with Chaotic Salp Swarm (OKELM), which categorizes the images into Viable, Non-tumor and Non-viable type tumor along with severity analysis. The proposed study used a PYTHON tool for implementation, and the simulation is done by utilizing publicly accessible histopathological images obtained from TCIA (The Cancer Imaging Archive). The performance of a proposed EL model is evaluated using accuracy, specificity, sensitivity, kappa, F-measure, ROC (AUC) etc. Thus, the proposed EL model achieved the highest results in terms of accuracy (98.505%) compared to other classifiers such as AdaBoost (91.70%), ELM (96.41%), KNN (86.90%), SVM (83.80%), and ANN (90.13%) respectively.