A deep learning-based medical image detection method, executed by an electronic device, the method comprising: acquiring a to-be-detected medical image, the to-be-detected medical image comprising a plurality of slices; extracting, for each of the plurality of slices in the to-be-detected medical image, N basic feature maps of the slice by using a deep neural network, N being an integer greater than 1; fusing, for each of the plurality of slices in the to-be-detected medical image, features of the N basic feature maps of the slice by using the deep neural network, to obtain M enhanced feature maps of the slice, M being an integer greater than 1; performing, for each of the plurality of slices in the to-be-detected medical image, a hierarchically dilated convolutions operation on each of the M enhanced feature maps of the slice by using the deep neural network, to generate a superposed feature map of each of the M enhanced feature maps of the slice; and predicting position information of a region of interest in the to-be-detected medical image and a confidence of the position information of the region of interest by using the deep neural network based on the superposed feature map of each of the plurality of slices in the to-be-detected medical image, wherein the N basic feature maps comprise A low-level feature maps and B high-level feature maps, each of A and B being an integer greater than 1; and the fusing the features of the N basic feature maps of the slice by using the deep neural network, to obtain the M enhanced feature maps of the slice comprises: performing convolution processing on an A th low-level feature map of the slice, to obtain a first high-level feature map of the slice as a first enhanced feature map; and for each i being an integer greater than or equal to 1 and smaller than A, performing convolution processing on an (A-i) th low level feature map of the slice, upsampling an i th high-level feature map of the slice, and adding a feature map obtained by performing convolution processing on the (A-i) th low level feature map and a feature map obtained by upsampling the i th high-level feature map, to obtain an (i+1) th high level feature map as an (i+1) th enhanced feature map. acquiring a to-be-detected medical image, the to-be-detected medical image comprising a plurality of slices; extracting, for each of the plurality of slices in the to-be-detected medical image, N basic feature maps of the slice by using a deep neural network, N being an integer greater than 1; fusing, for each of the plurality of slices in the to-be-detected medical image, features of the N basic feature maps of the slice by using the deep neural network, to obtain M enhanced feature maps of the slice, M being an integer greater than 1; performing, for each of the plurality of slices in the to-be-detected medical image, a hierarchically dilated convolutions operation on each of the M enhanced feature maps of the slice by using the deep neural network, to generate a superposed feature map of each of the M enhanced feature maps of the slice; and predicting position information of a region of interest in the to-be-detected medical image and a confidence of the position information of the region of interest by using the deep neural network based on the superposed feature map of each of the plurality of slices in the to-be-detected medical image, wherein the N basic feature maps comprise A low-level feature maps and B high-level feature maps, each of A and B being an integer greater than 1; and the fusing the features of the N basic feature maps of the slice by using the deep neural network, to obtain the M enhanced feature maps of the slice comprises: performing convolution processing on an A th low-level feature map of the slice, to obtain a first high-level feature map of the slice as a first enhanced feature map; and for each i being an integer greater than or equal to 1 and smaller than A, performing convolution processing on an (A-i) th low level feature map of the slice, upsampling an i th high-level feature map of the slice, and adding a feature map obtained by performing convolution processing on the (A-i) th low level feature map and a feature map obtained by upsampling the i th high-level feature map, to obtain an (i+1) th high level feature map as an (i+1) th enhanced feature map. performing convolution processing on an A th low-level feature map of the slice, to obtain a first high-level feature map of the slice as a first enhanced feature map; and for each i being an integer greater than or equal to 1 and smaller than A, performing convolution processing on an (A-i) th low level feature map of the slice, upsampling an i th high-level feature map of the slice, and adding a feature map obtained by performing convolution processing on the (A-i) th low level feature map and a feature map obtained by upsampling the i th high-level feature map, to obtain an (i+1) th high level feature map as an (i+1) th enhanced feature map.