This paper analyzes the effectiveness of a proposed quantum-hybrid model for early disease detection based on X-ray images. In the proposed approach, images representing diseased and healthy conditions are first generated. A Gabor filter is applied to each image to extract its spatial and directional characteristics. These features are then converted to quantum states using the flexible representation of quantum images quantum encoding technique. The resulting quantum representation is processed with a quantum neural network. The hybrid model classifies X-ray pictures as either “diseased” or “not diseased.“ The model's high sensitivity and specificity capabilities are demonstrated by the experimental results. This approach enables more efficient and faster medical diagnostics by leveraging quantum computing capabilities.