Lung cancer is one of the most common malignant tumors worldwide, with its incidence and mortality rates constantly rising. This is due to its unknown etiology, rapid onset, rapid metastasis, high malignancy, and difficulty in early diagnosis. By the middle and late stages, surgical options are lost, and the five-year survival rate is only about 15%. Early detection can increase the five-year survival rate to 70%–80%. Therefore, early detection, early diagnosis, and early treatment are crucial for improving lung cancer survival rates and reducing mortality. Early-stage lung cancer often presents with no specific symptoms, going largely unnoticed by doctors and patients, and commonly used diagnostic methods struggle to achieve early detection and definitive diagnosis. Currently, research on disease diagnosis based on electronic noses mainly focuses on the early diagnosis of kidney disease and diabetes, as well as the identification of certain bacterial types and growth stages. While electronic nose-based disease diagnosis has achieved significant results as an important direction for non-invasive medical diagnosis, there are currently no reports of certified respiratory diagnostic instruments. Further improvements in lung cancer diagnostic techniques and the effectiveness of various treatments have become top priorities in global oncology research. my country has also listed lung cancer as a key national research project. The goal of this electronic nose system is to find a more advanced instrument and technology that can detect and diagnose cancerous changes in local tissues. This paper focuses on the design of key technologies for an electronic nose system for early diagnosis of lung cancer, including the detection and collection of respiratory gases related to relevant pathologies, the selection and optimization of the gas sensor array, and the selection of pattern recognition technology. Good results have been achieved. 1. Structure of the Electronic Nose System for Early Diagnosis of Lung Cancer The electronic nose is an electronic system that identifies gases using the response patterns of a gas sensor array. It mainly consists of three functional components: a gas sampling manipulator, a gas sensor array, and a signal processing system. The main mechanism by which the electronic nose identifies gases is that each sensor in the array has a different sensitivity to the gas being measured, and the entire sensor array produces different response patterns for different gases. It is this difference that allows the system to identify odors based on the sensor response patterns. The typical workflow of an electronic nose is as follows: First, respiratory gas is drawn into a small container chamber containing an electronic sensor array using a respiratory gas collection device (after respiratory gas purification and flow control). Next, the initialized sensor array is exposed to the gas being measured. When volatile organic compounds (VOCs) come into contact with the active material surface of the sensor, an instantaneous response is generated. This response is recorded and transmitted to a signal processing unit for analysis. It is compared and identified with a large number of VOC patterns stored in a database to determine the gas type. Finally, a cleaning gas is used to rinse the surface of the sensor's active material to remove the measured gas mixture. Before proceeding to the next round of measurement, the sensors need to be re-initialized (i.e., each sensor needs to be cleaned with dry gas or some other reference gas before operation to reach a baseline state). The structure of the electronic nose early lung cancer diagnosis system is shown in Figure 1. Figure 1: Structure of the electronic nose early lung cancer diagnosis system. 2. Design of the electronic nose early lung cancer diagnosis system This paper focuses on the design of key technologies in lung cancer patients, including the detection of pathologically significant gases with strong correlation to the disease in their respiratory gases, the design of the respiratory gas collection device, the selection and optimization design of the gas sensor array, and the selection of pattern recognition technology. 2.1 Detection of Respiratory Gases Over 200 compounds have been detected in human exhaled breath, some of which are associated with lung cancer. Therefore, using exhaled breath for disease detection is a feasible method. Its advantages include non-invasiveness, simplicity, and speed, thus possessing high application potential. In conjunction with hospitals, selecting appropriate gas sensors and detection methods to detect the concentration of 22 characteristic volatile organic compounds (VOCs), such as styrene, decane, and undecane, in the exhaled breath of lung cancer patients is a promising non-invasive diagnostic and monitoring method for lung cancer. 2.2 Design of Respiratory Gas Collection Devices Because the concentration of lung cancer-related gases in exhaled breath is very low (usually at the ppb level), and traditional exhaled breath detection methods follow standard exhaled breath sampling procedures followed by gas chromatography-mass spectrometry (GC-MS) analysis to determine the types of compounds, this process requires concentrating a large amount of exhaled breath sample (approximately 3L) to reach the instrument's detectable limit. This method is not only expensive and time-consuming but also requires a large sample volume. The analysis cost required for an electronic nose is low, and the required breath sample volume is only about 10 ml. It is simple to operate and has a rapid response (within minutes). Breath gas collection plays a crucial role in the early lung cancer diagnosis system of an electronic nose. The structure of the gas collection device is shown in Figure 2. [align=center] Figure 2: Structure diagram of the gas collection device[/align] The arrows in Figure 2 indicate the flow direction of the cleaning gas and the breathing gas. After gas cleaning, the test subject's breath gas is inhaled through the air inlet. After a series of removals of moisture and irrelevant gases, the gas flow rate is controlled by a flow meter, and the gas is collected at timed intervals by a microprocessor. Then, an inactive gas is removed by a heater. 2.3 Selection and Optimization Design of the Gas Sensor Array In this electronic nose system, the gas sensor array is a key factor. The main factors affecting the performance of the gas sensor include materials and molding technology, the application of sol-gel technology to prepare the sensitive model, working conditions, and working environment. In addition, the influence of the initial process response and oxygen partial pressure on the characteristics of the gas sensor must also be considered. The performance of a gas sensor array directly determines the system's recognition capability, recognition range, and lifespan. Therefore, how to construct an array to improve the performance of an electronic nose system has become an important research topic. The selection of sensor array parameters mainly includes: array size, sensor type and its selectivity, stability, noise level, and thermal characteristics. The sensor array in an electronic nose system can be a monolithic integrated array or composed of multiple discrete components. When using a large number of array units, monolithic integrated arrays exhibit advantages such as small size and low power consumption; on the other hand, the performance of discrete devices is constantly improving. Regardless of the array type used, the array size and dimensions are crucial. Appropriately increasing the number of array units will result in better system recognition capability, but sometimes increasing the number of array units does not improve the system's recognition effect. Furthermore, larger arrays have higher power consumption and more severe thermal interference between units, which increases the difficulty of system integration. When constructing the array, the selectivity of each array unit must also be considered. If each unit has good selectivity for a specific gas, the array's recognition capability for those gases and their mixtures will be stronger, but the number of gas types it can recognize will decrease, and its recognition capability for complex mixtures with more components will be weaker. When constructing a sensor array, sensors with low selectivity and a wide response range can be used. Pattern recognition technology can be used to improve the system's selectivity and accuracy. Simultaneously, for different recognition objects, individual units with better selectivity can be added to simplify the array. Regarding array unit selection, methods such as normal distribution characteristics of test results, relative standard deviation analysis, and correlation coefficient analysis can be employed. In this system, cross-response characteristics and array stability are the main objectives for selecting sensor array units. 2.4 Selection of Pattern Recognition Technology Utilizing the cross-selectivity of gas sensors in the array to form high-dimensional response patterns for the measured medium, combined with pattern recognition technology, qualitative analysis of a single gas or determination of specific components in a gas mixture can be performed. Gas sensor responses typically exhibit strong nonlinearity, thus limiting conventional pattern recognition methods such as principal component analysis, partial least squares regression, and Euclidean clustering (most conventional classification methods are linear methods, assuming the response vector lies in Euclidean space, and the concentration of the measured object is linearly related to the sensor's response. This is only true when the concentration of gases and odors is very low). Artificial neural networks can handle nonlinear data, tolerate sensor drift and noise, exhibit good robustness, and have a higher prediction accuracy than conventional methods. Since the relationship between the sensor response value and the measured gas composition is very complex and difficult to express with explicit mathematical relationships, neural network technology is used to establish the mapping relationship between the sensor array response signal and the measured gas. Radial Basis Function (RBF) neural networks can overcome problems such as local minima and low efficiency to a certain extent, and have a significant advantage over BP neural networks in function approximation. Based on the above analysis, this system adopts the RBF neural network pattern recognition method. Figure 3 shows the RBF neural network topology. [align=center] Figure 3 RBF Neural Network Topology[/align] The RBF neural network consists of an input layer, intermediate layers (hidden layers), and an output layer. Here, the input layer only transmits data information without performing any transformation. The kernel function (or action function) of the hidden layer neurons is a Gaussian function, which performs spatial mapping transformation on the input information. The action function of the output layer neurons is a Sigmoid function, which linearly weights the information output by the hidden layer neurons and outputs it as the network's output result. A supervised learning method was used to train the neural network to determine the network's center, width, and adjustment weights. From the test samples, 60 out of 80 samples were randomly selected as the training set, and the remaining 20 as the test set. Three experiments were conducted under different temperature and humidity conditions. The network training parameters were momentum factor α = 0.09, learning factor η = 10.12, maximum training iterations of 20,000, target error of 0.01, and training time of approximately 3 minutes. The network reached the target error requirement. The trained network was tested on the samples, and the results are shown in Table 1. For the three experiments, the correct discrimination rate reached over 90%. This result is satisfactory, indicating that this application can detect lung cancer patients at an early stage. [align=center]Table 1 RBF Neural Network Discrimination Accuracy[/align] This paper establishes an electronic nose system that can quickly and accurately diagnose lung cancer. This electronic nose system consists of a sensor array. In data processing, the obtained sensor data was processed using an RBF neural network for pattern recognition. Three experiments were conducted under different temperature and humidity conditions. 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