TY - GEN
T1 - Discriminative Multi-feature Representation for Renal Cancer Detection based on Histopathology Images
AU - Cai, Jianxiu
AU - Zhang, Qi
AU - Zhang, Bob
AU - Cao, Bihui
AU - Liu, Manting
AU - Zhi, Cheng
AU - Che, Deji
AU - Zhu, Kangshun
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Renal cancer is one of the most common cancers in the world, and early diagnosis can increase the possibility of successful treatment and survival rate. However, manual detection is time-consuming and relies heavily on the experience of pathologists. Therefore, it is desirable to employ a computer-aided approach to automate the diagnostic process thereby saving time and labor. To date, a substantial amount of research with common deep learning methods have been applied to address this issue. However, deep learning methods require large numbers of images to train the model. Alternatively, traditional machine learning methods such as texture feature extractors can reach a reasonable result with a smaller computing cost. In this paper, we extensively study the efficiency of texture features extracted from histopathology images at detecting kidney cancer by adopting a weighted fusion method of HOG and GLCM, which includes both local structural features and full texture information from the histopathology images. We applied the proposed method on a histopathology image data set containing 93 patients with renal cancer and 150 patients with normal kidneys. The experimental results indicate that our method can achieve a similar outcome to deep learning methods, while reducing the computing time.
AB - Renal cancer is one of the most common cancers in the world, and early diagnosis can increase the possibility of successful treatment and survival rate. However, manual detection is time-consuming and relies heavily on the experience of pathologists. Therefore, it is desirable to employ a computer-aided approach to automate the diagnostic process thereby saving time and labor. To date, a substantial amount of research with common deep learning methods have been applied to address this issue. However, deep learning methods require large numbers of images to train the model. Alternatively, traditional machine learning methods such as texture feature extractors can reach a reasonable result with a smaller computing cost. In this paper, we extensively study the efficiency of texture features extracted from histopathology images at detecting kidney cancer by adopting a weighted fusion method of HOG and GLCM, which includes both local structural features and full texture information from the histopathology images. We applied the proposed method on a histopathology image data set containing 93 patients with renal cancer and 150 patients with normal kidneys. The experimental results indicate that our method can achieve a similar outcome to deep learning methods, while reducing the computing time.
KW - future fusion
KW - histopathology images
KW - renal cancer
KW - texture feature
UR - http://www.scopus.com/inward/record.url?scp=85125296458&partnerID=8YFLogxK
U2 - 10.1109/ICCC54389.2021.9674381
DO - 10.1109/ICCC54389.2021.9674381
M3 - Conference contribution
AN - SCOPUS:85125296458
T3 - 2021 7th International Conference on Computer and Communications, ICCC 2021
SP - 1848
EP - 1852
BT - 2021 7th International Conference on Computer and Communications, ICCC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on Computer and Communications, ICCC 2021
Y2 - 10 December 2021 through 13 December 2021
ER -