Deep semantic segmentation for skin cancer detection from hyperspectral images

As skin cancer types are a growing concern worldwide, a new screening tool combined with automation may help the clinicians in clinical examinations of lesions. A novel hyperspectral imager prototype has been noted to be a promising non-invasive tool in screening of lesions. Deep learning, especially semantic segmentation models, have brought successful results in other biomedical imaging tasks. Therefore, semantic segmentation could be used to automate the results from the hyperspectral images of lesions. In this thesis we used a novel hyperspectral image dataset of lesions that contained 61 images. The dataset contained 120 different wavebands from the spectral range of 450 − 850 nm with dimensions of 1920×1200 pixels. We implemented two different semantic segmentation models and compared their performance with the novel hyperspectral image data. The models were compared by their ability to segmentate the images and by their ability to classify lesion types from the images. From the implemented models, the combination of ResNet and Unet model architecture (ResNet-Unet) was able to segmentate the images more accurately with f1-score of 92.38 %, whereas the implemented Unet model gained f1-score of 92.17 %. In addition, the ResNet-Unet model classified the lesion types more accurately, and contained only one false negative result in melanoma classification, when the Unet model contained two false negatives in melanoma classification. This study was able to repeat the results of a previous study, where the segmentation model using hyperspectral image data was able to classify melanoma slightly more accurately than the clinicians in a previous study were.