The invention discloses an intracranial
aneurysm detection method and device based on a bottom double-
branch network and confidence coefficient calibration, and the method comprises the steps: carrying out the low-dimensional
feature extraction of an image in the bottom double-
branch network, and obtaining a feature map, dividing the feature map into a first sub-feature map and a second sub-feature map, and performing
feature extraction and fusion through different
convolution operation branches to obtain a final feature map; performing prediction and matching of a real bounding box through an optimal transmission
algorithm, and training a bottom double-
branch network according to a matching result; in the bottom double-branch network training process, confidence coefficient calibration is carried out at the output end of the model by constructing a
loss function based on confidence coefficient scores, and finally the trained bottom double-branch network is obtained to serve as an intracranial
aneurysm detection model; and inputting the new to-be-detected picture into the intracranial
aneurysm detection model, and detecting to obtain a high-confidence-coefficient intracranial aneurysm prediction bounding box so as to accurately position the intracranial aneurysm. According to the invention, the accuracy and reliability of intracranial aneurysm detection can be obviously improved.