This paper targets developing a Deep learning (DL) method for classifying encrypted traffic by utilizing FlowPics, which are visual representations of network traffic data. We leverage Convolutional Neural Networks (CNNs) to exploit the spatial features within FlowPics. However, traditional CNNs might not fully address channel-wise importance within feature maps. To address this, we incorporate Squeeze and Excitation (SE) blocks. SE blocks dynamically recalibrate feature map channels, emphasizing informative ones critical for encrypted traffic classification and suppressing less relevant ones. This targeted focus leads to improved performance compared to a simple CNN architecture. Our evaluation demonstrates robust performance across various encryption types (Non-VPN, VPN, and Tor) with minimal computational overhead. This work contributes to the field by showcasing the effectiveness of DL with CNNs enhanced by SE blocks for encrypted traffic classification.