A method for detecting a fundus image based on machine learning, comprising: acquiring a fundus image to be detected; classifying an entire region of the fundus image by using a first classification model to determine whether the fundus image contains a first feature or features, wherein the first feature or features indicate abnormal tissue or structure in the entire region of the fundus image; classifying, in parallel, one or more specific regions in the fundus image by using a second classification model per specific region to determine whether the fundus image contains a second feature or features, wherein the second feature or features indicate abnormal tissue or structure in the one or more specific regions of the fundus image, the specific region including at least one of an optic disc region, a macular region, a blood vessel region, a retinal region, and one or more regions within a set range, wherein a saliency of the first feature or features is greater than that of the second feature or features, the saliency is weighted by one or more of the following factors: chromatic aberration, contrast, grayscale, or a size of an occupied region; classifying the entire region of the fundus image by using a third classification model to determine whether the fundus image contains a third feature or features, wherein the saliency of the third features is smaller than that of the second features; and determining a detection result indicating the probability that the fundus image includes one or more of optic disc lesions, macular lesions and blood vessel lesions according to the classification results of the first classification model, the second classification model and the third classification model; and wherein the first classification model, the second classification model and the third classification model are respectively implemented as a convolutional neural network, and a maximum hidden layers of the convolutional neural network for the second features and third features are larger than a maximum hidden layer of the convolutional neural network for the first features. acquiring a fundus image to be detected; classifying an entire region of the fundus image by using a first classification model to determine whether the fundus image contains a first feature or features, wherein the first feature or features indicate abnormal tissue or structure in the entire region of the fundus image; classifying, in parallel, one or more specific regions in the fundus image by using a second classification model per specific region to determine whether the fundus image contains a second feature or features, wherein the second feature or features indicate abnormal tissue or structure in the one or more specific regions of the fundus image, the specific region including at least one of an optic disc region, a macular region, a blood vessel region, a retinal region, and one or more regions within a set range, wherein a saliency of the first feature or features is greater than that of the second feature or features, the saliency is weighted by one or more of the following factors: chromatic aberration, contrast, grayscale, or a size of an occupied region; classifying the entire region of the fundus image by using a third classification model to determine whether the fundus image contains a third feature or features, wherein the saliency of the third features is smaller than that of the second features; and determining a detection result indicating the probability that the fundus image includes one or more of optic disc lesions, macular lesions and blood vessel lesions according to the classification results of the first classification model, the second classification model and the third classification model; and wherein the first classification model, the second classification model and the third classification model are respectively implemented as a convolutional neural network, and a maximum hidden layers of the convolutional neural network for the second features and third features are larger than a maximum hidden layer of the convolutional neural network for the first features.