TY - GEN
T1 - Automated Gastrointestinal Tract Classification Via Deep Learning and the Ensemble Method
AU - Almanifi, Omair Rashed Abdulwareth
AU - Razman, Mohd Azraai Mohd
AU - Khairuddin, Ismail Mohd
AU - Abdullah, Muhammad Amirul
AU - Majeed, Anwar P.P.Abdul
N1 - Publisher Copyright:
© 2021 ICROS.
PY - 2021
Y1 - 2021
N2 - Colorectal cancer is a leading cause of death among the cancer family with a record of almost a million moralities in 2020 alone. While the treatment of colorectal cancer is very difficult, early diagnosis can help immensely with treatment, eliminating the risks, and recovery. In most cases early diagnosis is possible by catching any of the precursors of the disease, many of which appear on the Gastrointestinal tract. The use of machine learning to automate the process of gastrointestinal tract examination could accelerate the process of diagnosis, and increase its efficiency. This study suggests the use of the stacking ensemble method with multiple pre-trained CNN models for an accurate classification of GI tract using the publicly available dataset K vasir. The pre-trained models used in this study were ResNet50, MobileNetV2, and Xception, all of which were ensembled and trained on a subset of the data and tested on another to eliminate bias, and evaluates the model's capacity for generalization. Overall, the model demonstrated impressive performance at 99.2% accuracy, 0.9977 AUC, and 99.29% F1-score, especially compared to the individual constituent models and other models discussed in the review section of the study.
AB - Colorectal cancer is a leading cause of death among the cancer family with a record of almost a million moralities in 2020 alone. While the treatment of colorectal cancer is very difficult, early diagnosis can help immensely with treatment, eliminating the risks, and recovery. In most cases early diagnosis is possible by catching any of the precursors of the disease, many of which appear on the Gastrointestinal tract. The use of machine learning to automate the process of gastrointestinal tract examination could accelerate the process of diagnosis, and increase its efficiency. This study suggests the use of the stacking ensemble method with multiple pre-trained CNN models for an accurate classification of GI tract using the publicly available dataset K vasir. The pre-trained models used in this study were ResNet50, MobileNetV2, and Xception, all of which were ensembled and trained on a subset of the data and tested on another to eliminate bias, and evaluates the model's capacity for generalization. Overall, the model demonstrated impressive performance at 99.2% accuracy, 0.9977 AUC, and 99.29% F1-score, especially compared to the individual constituent models and other models discussed in the review section of the study.
KW - CNN
KW - Colorectal cancer
KW - Deep learning
KW - Ensemble
KW - Gastrointestinal tract
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85124192849&partnerID=8YFLogxK
U2 - 10.23919/ICCAS52745.2021.9649754
DO - 10.23919/ICCAS52745.2021.9649754
M3 - Conference Proceeding
AN - SCOPUS:85124192849
T3 - International Conference on Control, Automation and Systems
SP - 602
EP - 606
BT - 2021 21st International Conference on Control, Automation and Systems, ICCAS 2021
PB - IEEE Computer Society
T2 - 21st International Conference on Control, Automation and Systems, ICCAS 2021
Y2 - 12 October 2021 through 15 October 2021
ER -