Journal Article Epublish 8 citations
PubMed: 37835898 DOI: 10.3390/diagnostics13193155
Adapted Deep Ensemble Learning-Based Voting Classifier for Osteosarcoma Cancer Classification.
Diagnostics (Basel) 13(19)
Authors: Md Abul Ala Walid, Swarnali Mollick, Pintu Chandra Shill, Mrinal Kanti Baowaly, Md Rabiul Islam, Md Martuza Ahamad, Manal A Othman, Md Abdus Samad
Affiliations
- Department of Computer Science and Engineering, Khulna University of Engineering and Technology, Khulna 9203, Bangladesh.
- Department of Computer Science and Engineering, Northern University of Business and Technology, Khulna 9100, Bangladesh.
- Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh.
- Department of Biomedical Engineering, Islamic University, Kushtia 7003, Bangladesh.
- Medical Education Department, College of Medicine, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia.
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si 38541, Republic of Korea.
Abstract
The study utilizes osteosarcoma hematoxylin and the Eosin-stained image dataset, which is unevenly dispersed, and it raises concerns about the potential impact on the overall performance and reliability of any analyses or models derived from the dataset. In this study, a deep-learning-based convolution neural network (CNN) and adapted heterogeneous ensemble-learning-based voting classifier have been proposed to classify osteosarcoma. The proposed methods can also resolve the issue and develop unbiased learning models by introducing an evenly distributed training dataset. Data augmentation is employed to boost the generalization abilities. Six different pre-trained CNN models, namely MobileNetV1, Mo-bileNetV2, ResNetV250, InceptionV2, EfficientNetV2B0, and NasNetMobile, are applied and evaluated in frozen and fine-tuned-based phases. In addition, a novel CNN model and adapted heterogeneous ensemble-learning-based voting classifier developed from the proposed CNN model, fine-tuned NasNetMobile model, and fine-tuned Efficient-NetV2B0 model are also introduced to classify osteosarcoma. The proposed CNN model outperforms other pre-trained models. The Kappa score obtained from the proposed CNN model is 93.09%. Notably, the proposed voting classifier attains the highest Kappa score of 96.50% and outperforms all other models. The findings of this study have practical implications in telemedicine, mobile healthcare systems, and as a supportive tool for medical professionals.
MeSH
Neoplasms, Classification, Learning, Osteosarcoma