Rolling bearings are crucial components in mechanical systems. As low-frequency faults are less likely to occur, data samples related to those are rare, bringing difficulties to the collection and processing of related data. If not properly addressed, such faults can lead to severe safety hazards and substantial economic losses. To deal with this problem, this study proposes a dual-path fault diagnosis model that integrates traditional signal processing methods with convolutional neural network (CNN) and multilayer perceptron (MLP). In terms of feature extraction, the study employs a combination of discrete wavelet transform (DWT) and continuous wavelet transform (CWT), along with average downsampling techniques, to extract multi-scale time-frequency and time-domain features from the raw signals. The model contains two paths: one extracts time-frequency features of feature engineering by embedding the efficient channel attention (ECA) mechanism into the residual CNN, and the other uses MLP to process down-sampled multi-scale time-domain features, and finally fuses the two paths for classification. Small sample evaluation shows that the feature engineering method achieves an average diagnostic accuracy of 99.34% on the Case Western Reserve University (CWRU) dataset, which is higher than the 98.97% achieved by the traditional method. The hybrid CNN-MLP dual-path model achieves a high accuracy of 99.90% on the CWRU dataset and an accuracy of 98.38% on the Jiangnan University (JNU) dataset. It shows its application potential in small sample rolling bearing fault diagnosis.