<p>Breath analysis offers a promising noninvasive strategy for early cancer detection by capturing disease-specific volatile organic compound (VOC) signatures in exhaled breath. In this study, we developed a hierarchical deep convolutional neural network (HD-CNN)-based platform for dual-cancer classification using a multimodal gas sensor array. Clinical breath samples were collected from 206 participants, including 67 healthy controls (HC), 78 lung cancer (LC), and 61 gastric cancer (GC) patients. The sensor array, composed of semiconductor metal oxide (SMO), electrochemical (EC), and photoionization detector (PID) sensors, generated time-resolved signals that were converted into 2D response maps for classification. The HD-CNN model employed a two-stage structure: a coarse classifier to distinguish HC from cancer patients (CP), followed by a fine classifier to separate LC and GC. In 5-fold cross-validation, the HD-CNN achieved classification accuracies of 82.1% (HC), 84.0% (LC), and 88.1% (GC), and average AUCs of 0.89, 0.92, and 0.89, respectively. Compared to a 1D CNN, the HD-CNN demonstrated superior class separability, increased prediction confidence, and overall enhanced performance. Additionally, we evaluated multiple coarse–fine configurations and found that isolating HC in the first stage resulted in the highest overall performance. These results support the utility of hierarchical learning and multimodal sensing for robust and scalable breath-based multi-cancer screening.</p>