HEP-2 Specimen Image Segmentation and Classification Using GLCM and DCT Based Feature Extraction with CNN Classifier

Manju, C. C.; Jose, M. Victor

doi:10.1007/978-981-15-3992-3_12

Cited by 1 publication

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although there have been many related studies conducted in this field, including some that reached high accuracy [ 8 , 9 , 15 , 36 , 37 ], the classifications were not in accordance with well-established ICAP standards [ 1 , 2 ]. Another issue is that the question to be answered in previous studies was, “Which classification should this image belong to?” which was a “single choice question.” Most of the image datasets used were specifically prescreened well-defined IIF patterns with no mixed patterns.…”

Section: Discussionmentioning

confidence: 99%

Application of Supervised Machine Learning to Recognize Competent Level and Mixed Antinuclear Antibody Patterns Based on ICAP International Consensus

Sheu

Chung

et al. 2021

Diagnostics

View full text Add to dashboard Cite

Background: Antinuclear antibody pattern recognition is vital for autoimmune disease diagnosis but labor-intensive for manual interpretation. To develop an automated pattern recognition system, we established machine learning models based on the International Consensus on Antinuclear Antibody Patterns (ICAP) at a competent level, mixed patterns recognition, and evaluated their consistency with human reading. Methods: 51,694 human epithelial cells (HEp-2) cell images with patterns assigned by experienced medical technologists collected in a medical center were used to train six machine learning algorithms and were compared by their performance. Next, we choose the best performing model to test the consistency with five experienced readers and two beginners. Results: The mean F1 score in each classification of the best performing model was 0.86 evaluated by Testing Data 1. For the inter-observer agreement test on Testing Data 2, the average agreement was 0.849 (?) among five experienced readers, 0.844 between the best performing model and experienced readers, 0.528 between experienced readers and beginners. The results indicate that the proposed model outperformed beginners and achieved an excellent agreement with experienced readers. Conclusions: This study demonstrated that the developed model could reach an excellent agreement with experienced human readers using machine learning methods.

show abstract