Osteosarcoma is a highly aggressive primary bone malignancy whose diagnosis and treatment planning heavily rely on histopathology slide-level analysis. Manual analysis of histopathology slides is very time-consuming and also requires skilled pathologists to examine them. Recent advances in deep learning have demonstrated outstanding performance in digital pathology. However, most Convolutional Neural Network (CNN) approaches rely heavily on global pooling, which is unable to capture diagnostically critical areas. This study proposes an attention pooling-based patch-level classification for osteosarcoma histopathology images. The proposed model consists of two modules: The first is a hierarchical feature extractor that uses ConvNeXt tiny as its backbone for feature extraction, and the second is an attention pooling model that assigns adaptive weights to emphasize spatial importance within the deep feature maps. It allows the model to concentrate on diagnostically relevant areas. The experimental results demonstrate that the attention pooling-based feature flattening utilized in the proposed framework improves the performance. This proposed model is evaluated on an osteosarcoma tumor assessment dataset. The proposed model outperforms the existing models with an accuracy of 92.31%.