Medical Analysis
Using Hyperspectral Imaging to Detect Tumoral Epithelial Lesions

Tumor detection with hyperspectral imaging

Hyperspectral can see beyond the visible spectral and into the skin's molecular detail and structure to specify diseases such as melanoma.

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Detection of Tumoral Epithelial Lesions Using Hyperspectral Imaging and Deep Learning


In 2020, the estimated deaths caused by cancer in the US were 606,520. At roughly 2%, 11,480 of this total were on the skin. There are two types of skin cancer, and melanoma accounts for 74% of skin cancer deaths. Non-melanoma the remainder.

Melanoma suffers from a high probability of metastasis. Like all diseases, early detection is essential for increasing survival rates. Detection typically involves visual inspection and other dermoscopic devices. Dermatologists and clinicians use surface microscopes with a light source for a detailed view of the lesions. Further examinations such as blood tests, biopsies and imaging tests may be performed for proper diagnosis.

The limitations of such devices are that they do not quantify the skin parameters. Hyperspectral imaging is a non-invasive and non-ionising diagnostic device that can complement and augment current practices. Hyperspectral can see beyond the visible spectral and into the skin's molecular detail and structure to specify diseases such as melanoma.



For the analysis of epithelial tissue, the researchers in the study used a short-wave infrared (SWIR) hyperspectral camera with a spectral range of wavelengths between 900 and 2500 and a spectral resolution of 10 nm in the NIR and 6 nm in the SWIR region. This spectral band was selected for its ability to identify the larger molecules associated with cancer. Future studies could benefit from a higher spectral resolution to increase the sensitivity of the diagnosis.

The principle of hyperspectral imaging relies on the chemical differences between healthy and cancerous epithelial tissues or tumours. Different molecules reflect light differently. The hyperspectral camera software provides estimates of the absorbance value calculated from the measured reflectance intensity.

In this study, there were three sets of samples taken from human epithelial tissue. These samples were correlated with the hyperspectral imaging to train the classification algorithms.  The researchers used the RetinaNet neural network to support the classification and identification.


The proposed method of SWIR hyperspectral imaging showed promising results in distinguishing samples of melanoma. The machine learning technique proved able to extract spectral and spatial characteristics from tumour epithelial tissue lesion samples.

Future refinements are needed to improve the sensitivity and specificity of the analysis. Further research could increase the number of samples used to increase the spectral resolution and boost the diagnostic power closer to 100%.

Application notes