Lung cancer has become one of the malignant tumors with the highest morbidity and mortality rate worldwide. The early images of lung cancer are pulmonary nodules, and early detection and diagnosis can help reduce the incidence of lung cancer. However, due to the large differences in shape, size and location of pulmonary nodules in medical imaging, the detection of small nodules is very challenging. This paper proposes a new model, based on improved YOLOv5s, called YOLOv5-CASP. The proposed YOLOv5s improvements include: (i) incorporating Convolutional Block Attention Modules (CBAM) to suppress the interference features of the medical images through a channel dimension and spatial dimension, and to improve the detection accuracy of the model; (ii) optimizing the Spatial Pyramid Pooling - Fast (SPPF) module of YOLOv5s to Atrous Spatial Pyramid Pooling (ASPP) as to increase the model’s receptive field of images of different sizes and extract multi-scale contextual information for improving the detection performance on small lung nodules; and (iii) introducing a Contextual Transformer (CoT) module to optimize part of the CSPDarknet53 module of YOLOv5s in order to enhance the characteristics of the model while removing redundant operations extraction capacity. Experimental results conducted on two public datasets confirm that the proposed YOLOv5-CASP model outperforms the original YOLOv5s model and other five state-of-the-art models (Faster R-CNN, SSD, YOLOv4-Tiny, DETR-R50, Deformable DETR-R50), in terms of the mean average precision ( mAP ) and F1 score , by achieving corresponding values of 72% and 0.740 on the LUNA16 dataset, and 79.44% and 0.766 on the X-Nodule dataset.

Research on lung cancer automatic detection using deep learning algorithms has achieved good results but, due to the complexity of tumor edge features and possible changes in tumor positions, it is still a great challenge to diagnose patients with lung tumors based on computed tomography (CT) images. In order to solve the problem of scales and meet the requirements of real-time detection, an efficient one-stage model for automatic lung tumor detection in CT Images, called ELCT-YOLO, is presented in this paper. Instead of deepening the backbone or relying on a complex feature fusion network, ELCT-YOLO uses a specially designed neck structure, which is suitable to enhance the multi-scale representation ability of the entire feature layer. At the same time, in order to solve the problem of lacking a receptive field after decoupling, the proposed model uses a novel Cascaded Refinement Scheme (CRS), composed of two different types of receptive field enhancement modules (RFEMs), which enables expanding the effective receptive field and aggregate multi-scale context information, thus improving the tumor detection performance of the model. The experimental results show that the proposed ELCT-YOLO model has strong ability in expressing multi-scale information and good robustness in detecting lung tumors of various sizes.

The timely detection and segmentation of pulmonary nodules in lung computed tomography (CT) images can aid in the early diagnosis and treatment of lung cancer. However, manual segmentation of pulmonary nodules by doctors is highly demanding in terms of operational requirements and efficiency. To effectively improve the pulmonary nodule segmentation, this paper proposes a novel neural network, called ResDSda_U-Net, based on the original U-Net network with the following improvements: (1) combining a Depthwise Over-parameterized Convolutional layer (DO-Conv) with a simple parameter-free attention module (SimAM), in the form of a newly designed ResDS block; (2) introducing a dense atrous spatial pyramid pooling (DASPP) module, between the encoder and decoder, using modified dilated rates to extract multi-scale information more effectively; and (3) introducing channel and spatial attention mechanisms in the decoder, in the form of newly designed Convolution and Channel Attention (CCA) and Convolution and Spatial Attention (CSA) blocks, to enhance global pixel attention, fully capture global contextual information, and enable the decoder to better eliminate differences between pixels. The conducted experiments demonstrate that the proposed ResDSda_U-Net network outperforms all existing U-Net based networks (according to all evaluation metrics used) and all considered state-of-the-art networks (according to half of the evaluation metrics), by achieving corresponding values of 86.65% for the Dice Similarity Coefficient (DSC), 76.73% for Intersection over Union (IoU), 86.30% for sensitivity, and 87.22% for precision.

The increasing global demand for skin disease diagnostics emphasizes the urgent need for advancements in AI-assisted diagnostic technologies for dermatoscopic images. In current practical medical systems, the primary challenge is balancing lightweight models with accurate image analysis to address constraints like limited storage and computational costs. While knowledge distillation methods hold immense potential in healthcare applications, related research on multi-class skin disease tasks is scarce. To bridge this gap, our study introduces an enhanced multi-source knowledge fusion distillation framework, termed DSP-KD, which improves knowledge transfer in a dual-stage progressive distillation approach to maximize mutual information between teacher and student representations. The experimental results highlight the superior performance of our distilled ShuffleNetV2 on both the ISIC2019 dataset and our private skin disorders dataset. Compared to other state-of-the-art distillation methods using diverse knowledge sources, the DSP-KD demonstrates remarkable effectiveness with a smaller computational burden.