<p>Skin cancer image classification is known to be extremely complex due to the subtle visual differences between benign and malignant lesions. In this study, we propose a novel hybrid model that leverages the hierarchical feature extraction capabilities of the hybrid quantum convolutional neural network (HQCNN), the temporal dynamics captured by the bidirectional long short-term memory neural networks (BiLSTM) model, and the efficient feature extraction capabilities of MobileNetV2. We evaluated the proposed model on a clinically relevant skin cancer dataset, using images resized to 32 × 32 and 128 × 128 pixels to investigate the impact of resolution on classification performance. The HQCNN model augmented with BiLSTM and MobileNetV2 achieved a training accuracy of 97.7% and a test accuracy of 89.3% on 128 × 128-pixel color images, along with an F1 score of 89.81% and a recall of 94.33% for malignant cases, confirming clinical reliability and strong sensitivity in cancer detection. These results demonstrate robust feature extraction, improved contextual learning, and strong generalization for complex medical image classification tasks.</p>