Hyperspectral Imaging Combined with Artificial Intelligence in the Early Detection of Esophageal Cancer

Abstract

This study uses hyperspectral imaging (HSI) and a deep learning diagnosis model that can identify the stage of esophageal cancer and mark the locations. This model simulates the spectrum data from the image using an algorithm developed in this study which is combined with deep learning for the classification and diagnosis of esophageal cancer using a single-shot multibox detector (SSD)-based identification system. Some 155 white-light endoscopic images and 153 narrow-band endoscopic images of esophageal cancer were used to evaluate the prediction model. The algorithm took 19 s to predict the results of 308 test images and the accuracy of the test results of the WLI and NBI esophageal cancer was 88 and 91%, respectively, when using the spectral data. Compared with RGB images, the accuracy of the WLI was 83% and the NBI was 86%. In this study, the accuracy of the WLI and NBI was increased by 5%, confirming that the prediction accuracy of the HSI detection method is significantly improved.

Identification of Early Esophageal Cancer by Semantic Segmentation

“…Thus, early detection of EC is important for increasing the survival rate [ 10 , 11 ]. At present, endoscopists are unable to draw a conclusion from the endoscope images of the esophagus during the early stages of EC [ 12 ]. Therefore, the disease can go unnoticed in the earlier stages.…”

Intelligent Identification of Early Esophageal Cancer by Band-Selective Hyperspectral Imaging

“…HSI has previously been used in numerous classifications fields, such as agriculture [ 33 ], astronomy [ 34 ], military [ 35 ], biosensors [ 36 ], air pollution detection [ 37 , 38 ], remote sensing [ 39 ], dental imaging [ 40 ], environment monitoring [ 41 ], satellite photography [ 42 ], cancer detection [ 43 ], forestry monitoring [ 44 ], food security [ 45 ], natural resource surveying [ 46 ], vegetation observation [ 47 ], and geological mapping [ 48 ]. The advantage of HSI lies in its excellent resolution, and with a minimum spectral resolution of less than 10 nm, it can be used to obtain more information than RGB images [ 49 ].…”

Design of a Lab-On-Chip for Cancer Cell Detection through Impedance and Photoelectrochemical Response Analysis

“…Cancer is one of the most prominent fatal diseases, with more than 200 types [ 1 , 2 , 3 , 4 , 5 , 6 ]. In 2020, approximately 19.3 million new cancer cases and 10 million cancer deaths were reported [ 7 ].…”

“…With the increase in air pollution rates, the cancer rate has been steadily increasing, and traditional techniques, such as magnetic resonance imaging, ultrasounds, and biopsies, are insufficient for early-stage cancer diagnosis [ 11 ], are expensive and time consuming, and sometimes generate false negatives [ 12 , 13 , 14 ]. Therefore, artificial intelligence (AI) cancer diagnosis methods have been built, which have been successful [ 6 , 15 , 16 , 17 , 18 ]. Despite modern scientific developments, the survival rates of the cancer patients remain low because of the failure of early-stage detection [ 10 , 19 , 20 , 21 ].…”