Indeed, the contrast of white-light colposcope image is rather low, which would not be ideal for cervical cancer screening. According to the fact that cancerous tissue has a rich composition of hemoglobin, which has multiple characteristic spectral bands. In contrary to traditional hyperspectral imaging or sequential multi-spectral imaging, which needs scanning in the either spatial or spectral domain, here, we propose the utilization ofminiature snapshot narrow-band imaging (SNBI) method to expedite the spectral image acquisition process and enhance the contrast between different tissues. The goal is to realize a fast computer-aided diagnosis method for early screening of cervical cancer. Firstly, we usedan SNBI technology to capture the images of cervix tissues at four characteristic bands of hemoglobin, namely two of its absorption peaks at (415±10) and (525±10) nm, one reflectance peak at (620±10) nm, and one background band at (450±10) nm. Secondly, we fused those spectral images via simple algebric operation to enhance the contrast between normal and abnormal tissues. Thirdly, Euclidean distance algorithm was applied to the fused image to classify the tissues into different lesion grades. This is the first computer aided optical pathological diagnosis method with a diagnosis rate of over 20 fps. Herein, white-light colposcope, and miniature SNBI video camera were usedto separately capture images of fresh cervical tissues that were surgically dissected within 10 minutes. The same Euclidean distance classification algorithm was applied to the images obtained by the white light colposcope, and to the spectrally fused image obtained by the SNBI video camera. The classification accuracy of the two imaging methods was calculated and compared, using the histopathologic diagnosis as a standard reference. Euclidean classification accuracy upon the spectral fused image acquired by the SNBI was approximately 100%, which is undoubtedly better than that of the color image acquired by the conventional colposcopy. Multiple experienced gynecologists also subjectively agreed with the computer-generated classification upon the fused image, and highly acknowledged its clinical value especially on challenging areas where multiple degreed lesion layered together. The proposed SNBI method could improve the acquisition frame rate and contrast of the spectrally fused image, and effectively classify the cervical tissue into pathological-diagnosis-consistent types of tissues. Due to its advantages of being objective, intact and instant, SNBI has excellent potential to enlarge the coverage of cervical screening population in a low-income district and to assistprecise treatment of cervical cancer under image guidance.