Classification of osteosarcoma tumor from histological image using sequential RCNN

Osteosarcoma is an osseous tumor that occurs in the metaphyseal area around the knee accounts for roughly 20% of bone cancers mostly affects patients younger than 20 years. Early diagnosis of osteosarcoma cancer can pave the way for an unlimited choice of therapy opportunities. Moreover, pathological estimation of necrosis and tumor cells determines the future intensity of chemotherapy radiation to apply to patient. The biopsy confirms the diagnosis and divulges the grade of the tumor, necrotic, and non-tumor cells. Due to a lack of radiologists in third world countries like Bangladesh, it is extremely difficult to diagnose cancer in the early stage. Moreover, to identify the chemotherapy effect during the chemotherapy period, multiple radiologists are required which is quite expensive for most cancer hospitals. In this paper, a Sequential Recurrent Convolutional Neural Network (RCNN) model consisting of CNN and bidirectional Gated Recurrent Units (GRU) is proposed, which performs exceptionally well with small numbers of histopathological osteosarcoma Haematoxylin and Eosin (H E) stained images despite having the over-fitting problem, heterogeneity, intra-class variation, inter-class similarity, crowded context, the irregular shape of the nucleus and noisy data. Performance of the is compared with that of AlexNet, ResNet50, VGG16, LeNet and SVM models with the histopathological image dataset on osteosarcoma.