TY - GEN
T1 - Classification of osteosarcoma tumor from histological image using sequential RCNN
AU - Nabid, Rahad Arman
AU - Rahman, Md Latifur
AU - Hossain, Md Farhad
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/17
Y1 - 2020/12/17
N2 - 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.
AB - 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.
KW - Bidirectional GRU
KW - Osteosarcoma
KW - RCNN
UR - http://www.scopus.com/inward/record.url?scp=85104573309&partnerID=8YFLogxK
UR - https://ieeexplore.ieee.org/xpl/conhome/9393009/proceeding
U2 - 10.1109/ICECE51571.2020.9393159
DO - 10.1109/ICECE51571.2020.9393159
M3 - Conference contribution
AN - SCOPUS:85104573309
T3 - Proceedings of 2020 11th International Conference on Electrical and Computer Engineering, ICECE 2020
SP - 363
EP - 366
BT - Proceedings of 2020 11th International Conference on Electrical and Computer Engineering, ICECE 2020
PB - IEEE, Institute of Electrical and Electronics Engineers
T2 - 11th International Conference on Electrical and Computer Engineering, ICECE 2020
Y2 - 17 December 2020 through 19 December 2020
ER -