Hyperspectral Imaging for Enhanced Skin Cancer Classification Using Machine Learning

• Published in: Bioengineering (Basel) Vol. 12; no. 7; p. 755
• Main Authors: Lin, Teng-Li, Mukundan, Arvind, Karmakar, Riya, Avala, Praveen, Chang, Wen-Yen, Wang, Hsiang-Chen
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 11.07.2025; MDPI
• Subjects: Accuracy; Algorithms; Artificial neural networks; Cancer; Classification; Color; Comparative analysis; convolutional neural network; Data mining; Datasets; Diagnosis; Geospatial data; Hyperspectral imaging; Learning algorithms; Lesions; Machine learning; Medical imaging; Medical imaging equipment; Melanoma; Neural networks; Polynomials; random forest; Skin cancer; Skin diseases; Skin lesions; spectrum-aided vision enhancer; Support vector machines; yolo; Taiwan; skin cancer; yolo; convolutional neural network; narrow-band imaging; spectrum-aided vision enhancer; random forest; hyperspectral imaging; band selection

Objective: The classification of skin cancer is very helpful in its early diagnosis and treatment, considering the complexity involved in differentiating AK from BCC and SK. These conditions are generally not easily detectable due to their comparable clinical presentations. Method: This paper presen...