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Healthcare Professional in the Loop (HPIL): Classification of Standard and Oral Cancer-Causing Anomalous Regions of Oral Cavity Using Textural Analysis Technique in Autofluorescence Imaging.

, , , , , , , , , and . Sensors, 20 (20): 5780 (2020)

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Clinically Guided Trainable Soft Attention for Early Detection of Oral Cancer., , , , , , , , , and 5 other author(s). CAIP (1), volume 13052 of Lecture Notes in Computer Science, page 226-236. Springer, (2021)D'OraCa: Deep Learning-Based Classification of Oral Lesions with Mouth Landmark Guidance for Early Detection of Oral Cancer., , , , , , , , , and 7 other author(s). MIUA, volume 12722 of Lecture Notes in Computer Science, page 408-422. Springer, (2021)Automated Detection and Classification of Oral Lesions Using Deep Learning for Early Detection of Oral Cancer., , , , , , , , , and 6 other author(s). IEEE Access, (2020)Healthcare Professional in the Loop (HPIL): Classification of Standard and Oral Cancer-Causing Anomalous Regions of Oral Cavity Using Textural Analysis Technique in Autofluorescence Imaging., , , , , , , , , and . Sensors, 20 (20): 5780 (2020)Fine-Tuning Deep Learning Architectures for Early Detection of Oral Cancer., , , , , , , , , and 6 other author(s). ISMCO, volume 12508 of Lecture Notes in Computer Science, page 25-31. Springer, (2020)