A micro-hyperspectral image classification method of gallbladder cancer based on multi-scale fusion attention mechanism

Abstract

： Objective Gallbladder carcinoma is recognized as one of the most malignant tumors in relevant to biliary sys⁃ tem. Its prognosis is extremely poor ， and only 6 months of overall average. It is challenged for missed diagnose because of the lack of typical clinical manifestations in early stage of gallbladder cancer. To clarify gallbladder lesions for early detec⁃ tion of gallbladder carcinoma accurately ， current gallbladder cancer - related diagnosis is mainly focused on the interpreta⁃ tion of digital pathological section images （ such as b - ultrasound ， computed tomography （ CT ）， magnetic resonance imaging （ MRI ）， etc. ） in terms of the computer - aided diagnosis （ CAD ） . However ， the accuracy is quite lower because the molecu⁃ lar level information of diseased organs cannot be obtained. Micro - hyperspectral technology can be incorporated the fea⁃ tures of spectral analysis and optical imaging ， and it can obtain the chemical composition and physical features for biologi⁃ cal tissue samples at the same time. The changes of physical attributes of cancerous tissue may not be clear in the early stage ， but the changes of chemical factors like its composition ， structure and content can be reflected by spectral informa⁃ tion. Therefore ， micro hyperspectral imaging has its potentials to achieve the early diagnosis of cancer more accurately. Micro - hyperspectral technology ， as a special optical diagnosis technology ， can provide an effective auxiliary diagnosis method for clinical research. However ， it can provide richer spectral information but large amount of data and information redundancy are increased. To develop an improved accuracy detection method and use the rich spatial and hyperspectral information effectively ， we design a multi - scale fusion attention mechanism - relevant network model for gallbladder cancer - oriented classification accuracy optimization. Method The multiscale squeeze - and - excitation - residual （ MSE - Res ） can be used to realize the fusion of multiscale features between channel dimensions. First ， an improved multi - scale feature extrac⁃ tion module is employed to extract features of different scales in channel dimension. To extract the salient features of the image a maximum pooled layer ， an upper sampling layer is used beyond convolution layer of 1 × 1. To compensate for the