Electronic noses (e-noses) have proven effective in detecting lung cancer by analyzing Volatile Organic Compounds (VOCs) in breath samples, with Multivariate Time Series Classification (MTSC) as the primary task. However, challenges remain in effectively capturing spatiotemporal information for MTSC. To address this, a novel method called SIR-3DCNN for MTSC in lung cancer detection is introduced. A pivotal aspect of SIR-3DCNN is the Sensor Array Optimization (SAO), an algorithm based on Linear Discriminant Analysis (LDA) that can decrease the number of sensors from 22 to 8 while increasing accuracy by 2.35 percentage points in our study. Furthermore, SIR-3DCNN incorporates an advanced technique for representing spatiotemporal information, converting optimized Multivariate Time Series (MTS) into Maximum Trajectory Matrix Images (MTMIs) and arranging them to maximize the Sum of Inter-Frame Mutual Information (SIFMI). Additionally, we have developed C3D-Light, a lightweight yet effective 3D Convolutional Neural Network (3DCNN) that demonstrates superior performance compared to other models. Comparative analyses with state-of-the-art methodologies reveal that SIR-3DCNN consistently outperforms existing methods, achieving perfect sensitivity (100%), the highest specificity (80%), accuracy (92.94%), AUC (94.85%), precision (90.26%), and F1-score (94.86%) among all compared methods. This advancement holds significant promise for lung cancer detection using electronic noses. The source code is available at https://github.com/cqu-3dteam/sir-3dcnn.

Thyroid-related diseases, particularly thyroid cancer, are rising globally, emphasizing the critical need for the early detection and accurate screening of thyroid nodules. Ultrasound imaging has inherent limitations—high noise, low contrast, and blurred boundaries—that make manual interpretation subjective and error-prone. To address these challenges, YOLO-Thyroid, an improved model for the automatic detection of thyroid nodules in ultrasound images, is presented herein. Building upon the YOLOv8 architecture, YOLO-Thyroid introduces the C2fA module—an extension of C2f that incorporates Coordinate Attention (CA)—to enhance feature extraction. Additionally, loss functions were incorporated, including class-weighted binary cross-entropy to alleviate class imbalance and SCYLLA-IoU (SIoU) to improve localization accuracy during boundary regression. A publicly available thyroid ultrasound image dataset was optimized using format conversion and data augmentation. The experimental results demonstrate that YOLO-Thyroid outperforms mainstream object detection models across multiple metrics, achieving a higher detection precision of 54%. The recall, calculated based on the detection of nodules containing at least one feature suspected of being malignant, reaches 58.2%, while the model maintains a lightweight structure. The proposed method significantly advances ultrasound nodule detection, providing an effective and practical solution for enhancing diagnostic accuracy in medical imaging.

Natural disasters create complex environments where effective human detection is both critical and challenging, especially when individuals are partially occluded. While recent advancements in computer vision have improved detection capabilities, there remains a significant need for efficient solutions that can enhance search-and-rescue (SAR) operations in resource-constrained disaster scenarios. This study modified the original DINO (Detection Transformer with Improved Denoising Anchor Boxes) model and introduced the visibility-enhanced DINO (VE-DINO) model, designed for robust human detection in occlusion-heavy environments, with potential integration into SAR system. VE-DINO enhances detection accuracy by incorporating body part key point information and employing a specialized loss function. The model was trained and validated using the COCO2017 dataset, with additional external testing conducted on the Disaster Occlusion Detection Dataset (DODD), which we developed by meticulously compiling relevant images from existing public datasets to represent occlusion scenarios in disaster contexts. The VE-DINO achieved an average precision of 0.615 at IoU 0.50:0.90 on all bounding boxes, outperforming the original DINO model (0.491) in the testing set. The external testing of VE-DINO achieved an average precision of 0.500. An ablation study was conducted and demonstrated the robustness of the model subject when confronted with varying degrees of body occlusion. Furthermore, to illustrate the practicality, we conducted a case study demonstrating the usability of the model when integrated into an unmanned aerial vehicle (UAV)-based SAR system, showcasing its potential in real-world scenarios.