Classification of Skin Cancer Using Novel Hyperspectral Imaging Engineering via YOLOv5

Abstract

Many studies have recently used several deep learning methods for detecting skin cancer. However, hyperspectral imaging (HSI) is a noninvasive optics system that can obtain wavelength information on the location of skin cancer lesions and requires further investigation. Hyperspectral technology can capture hundreds of narrow bands of the electromagnetic spectrum both within and outside the visible wavelength range as well as bands that enhance the distinction of image features. The dataset from the ISIC library was used in this study to detect and classify skin cancer on the basis of basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and seborrheic keratosis (SK). The dataset was divided into training and test sets, and you only look once (YOLO) version 5 was applied to train the model. The model performance was judged according to the generated confusion matrix and five indicating parameters, including precision, recall, specificity, accuracy, and the F1-score of the trained model. Two models, namely, hyperspectral narrowband image (HSI-NBI) and RGB classification, were built and then compared in this study to understand the performance of HSI with the RGB model. Experimental results showed that the HSI model can learn the SCC feature better than the original RGB image because the feature is more prominent or the model is not captured in other categories. The recall rate of the RGB and HSI models were 0.722 to 0.794, respectively, thereby indicating an overall increase of 7.5% when using the HSI model.

Advancing Dermatological Diagnosis: Development of a Hyperspectral Dermatoscope for Enhanced Skin Imaging

“…In the recent past, multiple research projects have been carried out in the field of HSI for skin lesions. The work is very wide-ranging, from the differentiation of benign and malignant skin lesions [17][18][19][20][21] , the classification of skin lesions [22][23][24][25] , to the estimation of skin chromophores and other biological parameters 26,27 . The work differs not only in the application, but also greatly in the devices used.…”

Automatic Counterfeit Currency Detection Using a Novel Snapshot Hyperspectral Imaging Algorithm

“…HSI acquires the spectrum for each pixel in an image [ 18 , 19 , 20 , 21 ]. It has been used in many applications, such as cancer detection [ 22 , 23 , 24 , 25 ], air pollution monitoring [ 26 , 27 ], nanostructure identification [ 28 , 29 , 30 , 31 ], aerospace [ 32 , 33 , 34 ], food quality maintenance [ 35 ], verification [ 36 , 37 , 38 ], military [ 39 ], remote sensing [ 40 , 41 , 42 ], and agriculture [ 43 ].…”

Hyperspectral imaging applied to YOLOv5 to identify early esophageal cancer

“…Compared with traditional images that only have three channels of red, green and blue information, hyperspectral imaging analyzes more detailed spectral information [8,9]. It has been used in air pollution detection [10,11], agriculture [12,13], medical imaging [14,15,16] and other applications.…”