An artificial neural network-based method for detecting a surface type of an object includes: receiving a plurality of object images, wherein a plurality of spectra of the plurality of object images are different from one another and each of the object images has one of the spectra; transforming each object image into a matrix, wherein the matrix has a channel value that represents the spectrum of the corresponding object image; and executing a deep learning program by using the matrices to build a predictive model for identifying a target surface type of the object. Accordingly, the speed of identifying the target surface type of the object is increased, further improving the product yield of the object.

An artificial neural network-based method for detecting a surface type of an object includes: receiving a plurality of object images, wherein a plurality of spectra of the plurality of object images are different from one another and each of the object images has one of the spectra; transforming each object image into a matrix, wherein the matrix has a channel value that represents the spectrum of the corresponding object image; and executing a deep learning program by using the matrices to build a predictive model for identifying a target surface type of the object. Accordingly, the speed of identifying the target surface type of the object is increased, further improving the product yield of the object.

A method determines whether a cell shown in a nuclear fluorescence image acquired through a confocal microscope is a tumorous cell. The method is based on the application of a discrete Wavelet transform to a reference matrix associated with a reference image of the nucleus of the cell, obtained by inserting a segmented image of the nucleus on a background of a predetermined color, to obtain four further matrices, and on the generation of a respective co-occurrence matrix for each further statistical function. The matrices characterize the nucleus of the cell and are calculated starting from each co-occurrence matrix. The results are provided as input to a predetermined neural network (NN).

A method determines whether a cell shown in a nuclear fluorescence image acquired through a confocal microscope is a tumorous cell. The method is based on the application of a discrete Wavelet transform to a reference matrix associated with a reference image of the nucleus of the cell, obtained by inserting a segmented image of the nucleus on a background of a predetermined color, to obtain four further matrices, and on the generation of a respective co-occurrence matrix for each further statistical function. The matrices characterize the nucleus of the cell and are calculated starting from each co-occurrence matrix. The results are provided as input to a predetermined neural network (NN).

An artificial neural network-based method for selecting a surface type of an object is suitable for selecting a plurality of objects. The artificial neural network-based method for selecting a surface type of an object includes performing surface type identification on a plurality of object images by using a plurality of predictive models to obtain a prediction defect rate of each of the predictive models, wherein the object images correspond to surface types of a part of the objects, and cascading the predictive models according to the respective prediction defect rates of the predictive models into an artificial neural network so as to select the remaining objects.

An artificial neural network-based method for selecting a surface type of an object is suitable for selecting a plurality of objects. The artificial neural network-based method for selecting a surface type of an object includes performing surface type identification on a plurality of object images by using a plurality of predictive models to obtain a prediction defect rate of each of the predictive models, wherein the object images correspond to surface types of a part of the objects, and cascading the predictive models according to the respective prediction defect rates of the predictive models into an artificial neural network so as to select the remaining objects.

Assessment of pulmonary function in coronavirus patients includes use of a computer aided diagnostic system to assess pulmonary function and risk of mortality in patents with coronavirus disease 2019. The CAD system processes thoracic X-ray data from a patient, extracts imaging markers, and grades disease severity based at least in part on the extracted imaging markers, thereby distinguishing between higher risk and lower risk patients.

A method for characterising a region of interest comprising adipose tissue using medical imaging data, e.g. based on computer tomography (CT) of a subject is disclosed. The method comprises calculating the value of a radiomic signature of the region of interest using the medical imaging data. Also disclosed is a method for deriving a radiomic signature indicative of adipose tissue dysfunction. The method comprises obtaining a radiomic dataset and using the radiomic dataset to construct a radiomic signature of a region of interest comprising adipose tissue. Also disclosed are systems for performing the aforementioned methods.

The disclosure belongs to the technical field of radiomics, and particularly relates to an extraction method for radiomics feature information of knee joint effusion. each layer of an image is segmented into a plurality of regions, the interference of image noises in each region is removed, then whether each region after interference removal is an effusion region or not is judged, finally, the radiomics features of each effusion region are calculated, interpolation processing is respectively implemented on the obtained image position and the area of the effusion regions, an effusion area simulation change curve is drawn, curve integration is implemented to obtain volume information, and all the extracted information is stored in a cell array of a MATLAB. thus the effusion information in the MRI image of the knee joint is automatically extracted, and meanwhile, the method is fast in speed, high in accuracy, good in repeatability.

An artificial neural network-based method for detecting a surface pattern of an object includes receiving a plurality of object images, dividing each object image into a plurality of image areas, designating at least one region of interest from the plurality of image areas of each of the object images, and performing deep learning with the at least one region of interest to build a predictive model for identifying a surface pattern of the object.