Comparative analysis of traditional machine learning and automated machine learning: advancing inverted papilloma versus associated squamous cell carcinoma diagnosis
Inverted papilloma conversion to squamous cell carcinoma is not always easy to predict. AutoML requires much less technical knowledge and skill to use than traditional ML. AutoML surpassed the traditional ML algorithm in differentiating IP from IP-SCC.
Authors
- Jayakar V. Nayak (ORCID: https://orcid.org/0000-0003-4454-0861)
- Peter H. Hwang (ORCID: https://orcid.org/0000-0002-0786-4675)
- Michael A. Kohanski (ORCID: https://orcid.org/0000-0001-8399-364X)
- Michael T. Chang (ORCID: https://orcid.org/0000-0001-9519-5880)
- Farideh Hosseinzadeh (ORCID: https://orcid.org/0000-0003-1555-4711)
- Zara M. Patel (ORCID: https://orcid.org/0000-0003-2072-982X)
- S. Saeed Mohammadi (ORCID: https://orcid.org/0000-0002-5996-730X)
- Nithin D. Adappa (ORCID: https://orcid.org/0000-0002-0339-8769)
- James Palmer
Institutions
- Smith-Kettlewell Eye Research Institute (US)
- University of Pennsylvania (US)
- Stanford University (US)
Publication Details
- Journal
- International Forum of Allergy & Rhinology
- Published
- 2024-08-26
- DOI
- https://doi.org/10.1002/alr.23438
- Citations
- 5
- Primary Topic
- Medical Imaging and Pathology Studies
- Type
- article
- Field-Weighted Citation Impact
- 2.09