Department of Computing and Mathematics, University of São Paulo, Bandeirantes Av. 3900, Monte Alegre, Ribeirão Preto, SP 14040-901, Brazil. Electronic address:
Published: April 2023
AI Article Synopsis
Article Abstract
Background And Objective: Current studies based on digital biopsy images have achieved satisfactory results in detecting colon cancer despite their limited visual spectral range. Such methods may be less accurate when applied to samples taken from the tumor margin region or to samples containing multiple diagnoses. In contrast with the traditional computer vision approach, micro-FTIR hyperspectral images quantify the tissue-light interaction on a histochemical level and characterize different tissue pathologies, as they present a unique spectral signature. Therefore, this paper investigates the possibility of using hyperspectral images acquired over micro-FTIR absorbance spectroscopy to characterize healthy, inflammatory, and tumor colon tissues.
Methods: The proposed method consists of modeling hyperspectral data into a voxel format to detect the patterns of each voxel using fully connected deep neural network. A web-based computer-aided diagnosis tool for inference is also provided.
Results: Our experiments were performed using the K-fold cross-validation protocol in an intrapatient approach and achieved an overall accuracy of 99% using a deep neural network and 96% using a linear support vector machine. Through the experiments, we noticed the high performance of the method in characterizing such tissues using deep learning and hyperspectral images, indicating that the infrared spectrum contains relevant information and can be used to assist pathologists during the diagnostic process.
We report a hyperspectral Raman imaging lidar system that can remotely detect and identify typical plastic species. The system is based on a frequency-doubled, Q-switched Nd:YAG laser operating at 532 nm and an imaging spectrograph equipped with a gated intensified CCD spectrometer. Stand-off detection of plastics is achieved at 6 m away with a relatively wide field of view of 1 × 150 mm, thus providing the groundwork for better solutions in monitoring marine plastic pollution.
Hyperspectral (HS) imaging bridges conventional imaging into spectroscopy and generates a spatial map of spectral variations. On the one hand, in HS imaging, the effect of the background on the final spectra has to be removed or managed. On the other hand, there are important classes of materials that need to be immobilized for investigation.
The unique fatty acid composition of BSF larvae oil makes it suitable for various applications, including use in animal feed, aquaculture, biodiesel production, biomaterials, and the food industry. Determination of BSF larvae composition usually requires analytical methods with chemicals, thus needing emerging techniques for fast characterization of its composition. In this study, Near Infrared Hyperspectral Imaging (NIR-HSI) (928 - 2524 nm) coupled with chemometrics was applied to predict the lipid content and fatty acid composition in intact black soldier fly (BSF) larvae.
In order to achieve precise discrimination of leaf diseases in the Maize/Soybean intercropping system, i.e. leaf spot disease, rust disease, mixed leaf diseases, this study utilized hyperspectral imaging and deep learning algorithms for the classification of diseased leaves of maize and soybean.
Photosensitizing compounds are eco-friendly promising organic dyes for managing insect pests without facing the risk of resistance. The photodynamic efficacy of four Photosensitizing compounds (rose Bengal, rhodamine B, methylene blue and methyl violet) was monitored against the third larval instar of Spodoptera littoralis (Boisduval), after exposure to sunlight. The LC values of the four compounds; rose Bengal, rhodamine B, methylene blue and methyl violet recorded 0.
