Identification of basal cell carcinoma skin cancer using FTIR and Machine learning

Author(s)

Peres, Daniella Lumara ; Farooq, Sajid ; Raffaeli, Rocio ; Croce, Maria Virginia ; Croce, Adela E. ; Zezell, Denise Maria ; IEEE

• Document type: Journal article
• Source: 2023 INTERNATIONAL CONFERENCE ON OPTICAL MEMS AND NANOPHOTONICS, OMN AND SBFOTON INTERNATIONAL OPTICS AND PHOTONICS CONFERENCE, SBFOTON IOPC; v. N/A, p. 2-pg., 2023-01-01.

Abstract

Here we applied ATR-FTIR spectroscopy combined with computational modeling based on 3D-discriminant analysis (3D-PCA-QDA). Our results present an exceptional performance of 3D-discriminant algorithms to diagnose BCC skin cancer, indicating the accuracy up to 99%. (AU)

• FAPESP's process: 21/00633-0 - Hyperspectral signal processing and analysis applied to histopathological diagnosis
• Grantee: Luciano Bachmann
• Support Opportunities: Regular Research Grants
• FAPESP's process: 17/50332-0 - Scientific, technological and infrastructure qualification in radiopharmaceuticals, radiation and entrepreneurship for health purposes (PDIp)
• Grantee: Marcelo Linardi
• Support Opportunities: Research Grants - State Research Institutes Modernization Program