The advent of quantum machine learning has led to the development of quantum convolutional neural networks (QCNNs) for image classification across various domains. A key limitation of current quantum hardware is the limited number of available qubits and the bandwidth required to load data, necessitating classical dimensionality reduction before inputting image data into quantum circuits. The classification performance varies significantly depending on the quantum circuit and the parameters used. We propose integrating a classical encoder for dimensionality reduction before the QCNN and training it end-to-end. Tested against principal component analysis and autoencoder methods on the Pneumonia MedMNIST dataset, our approach improves performance and stabilizes results across different quantum circuits. While our method falls short compared to the classical baseline in terms of performance, it uses significantly fewer parameters.