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• To design an automatic diagnosis system using ensemble machine learning classifiers to detect bone cancer (Osteosarcoma) in histopathological images to diagnose cancer in the early stage. • To propose a reliable and accurate solution for bone tumor detection using Ensemble Learning Models, namely KSVM, IANN_BSO, and OKELM, combined with Severity analysis. • To perform effective image enhancement and noise removal based filtering approach to minimize the error and processing time. • To improve the accuracy of classification using ensemble models and to identify the stage severity to maximize the detection performance with the integration of segmentation, feature extraction and selection techniques. 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. [ABSTRACT FROM AUTHOR]

• To design an automatic diagnosis system using ensemble machine learning classifiers to detect bone cancer (Osteosarcoma) in histopathological images to diagnose cancer in the early stage. • To propose a reliable and accurate solution for bone tumor detection using Ensemble Learning Models, namely KSVM, IANN_BSO, and OKELM, combined with Severity analysis. • To perform effective image enhancement and noise removal based filtering approach to minimize the error and processing time. • To improve the accuracy of classification using ensemble models and to identify the stage severity to maximize the detection performance with the integration of segmentation, feature extraction and selection techniques. 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. [ABSTRACT FROM AUTHOR]