Artificial intelligence auxiliary diagnosis and treatment system for breast cancer in developing countries.

BACKGROUND: In many developing countries, a significant number of breast cancer patients are unable to receive timely treatment due to a large population base, high patient numbers, and limited medical resources. OBJECTIVE: This paper proposes a breast cancer assisted diagnosis system based on electronic medical records. The goal of this system is to address the limitations of existing systems, which primarily rely on structured electronic records and may miss crucial information stored in unstructured records. METHODS: The proposed approach is a breast cancer assisted diagnosis system based on electronic medical records. The system utilizes breast cancer enhanced convolutional neural networks with semantic initialization filters (BC-INIT-CNN). It extracts highly relevant tumor markers from unstructured medical records to aid in breast cancer staging diagnosis and effectively utilizes the important information present in unstructured records. RESULTS: The model's performance is assessed using various evaluation metrics. Such as accuracy, ROC curves, and Precision-Recall curves. Comparative analysis demonstrates that the BC-INIT-CNN model outperforms several existing methods in terms of accuracy and computational efficiency. CONCLUSIONS: The proposed breast cancer assisted diagnosis system based on BC-INIT-CNN showcases the potential to address the challenges faced by developing countries in providing timely treatment to breast cancer patients. By leveraging unstructured medical records and extracting relevant tumor markers, the system enables accurate staging diagnosis and enhances the utilization of valuable information.

Continuous Refinement-based Digital Pathology Image Assistance Scheme in Medical Decision-Making Systems.

Digital pathology images' extensive cellular information provide a trustworthy foundation for tumor diagnosis. With the aid of computer-aided diagnostics, pathologists can locate crucial information more quickly. The cascade structure refines the segmentation results by utilizing its multi-task and multi-stage characteristics. However, cascade-based models require downsampling and cropping of patches during the inference process due to the ultra-high resolution and complex structure of pathology images. This not only increases the cost and computation time but also results in the loss of cellular details and corrupts the global contextual information. This study proposes a Digital Pathology Image Assistance Program (CRSDPI) for medical decision-making systems that is based on continuous improvement. After locating the region of interest using the maximum inter-class variance method, the pictures are preprocessed to account for the impacts of staining inconsistencies and sensitivity variations on the model's performance. Ultimately, we create a two-phase continuously refined segmentation network (TCRNet) by combining an enhanced continuous refinement model with a coarse segmentation network built on a pyramid scene parsing network. The coarse segmentation network introduces an auxiliary loss term to speed up convergence, and the refined model introduces an implicit function to reduce computational cost and reconstruct more details. The TCRNet model refines the target by successively aligning the features without the need to take cascading decoder operations after encoder. Experiments conducted on digital pathology images of breast cancer and osteosarcoma demonstrate the superior prediction accuracy and computational speed of our strategy.

A semantic fidelity interpretable-assisted decision model for lung nodule classification.

PURPOSE: Early diagnosis of lung nodules is important for the treatment of lung cancer patients, existing capsule network-based assisted diagnostic models for lung nodule classification have shown promising prospects in terms of interpretability. However, these models lack the ability to draw features robustly at shallow networks, which in turn limits the performance of the models. Therefore, we propose a semantic fidelity capsule encoding and interpretable (SFCEI)-assisted decision model for lung nodule multi-class classification. METHODS: First, we propose multilevel receptive field feature encoding block to capture multi-scale features of lung nodules of different sizes. Second, we embed multilevel receptive field feature encoding blocks in the residual code-and-decode attention layer to extract fine-grained context features. Integrating multi-scale features and contextual features to form semantic fidelity lung nodule attribute capsule representations, which consequently enhances the performance of the model. RESULTS: We implemented comprehensive experiments on the dataset (LIDC-IDRI) to validate the superiority of the model. The stratified fivefold cross-validation results show that the accuracy (94.17%) of our method exceeds existing advanced approaches in the multi-class classification of malignancy scores for lung nodules. CONCLUSION: The experiments confirm that the methodology proposed can effectively capture the multi-scale features and contextual features of lung nodules. It enhances the capability of shallow structure drawing features in capsule networks, which in turn improves the classification performance of malignancy scores. The interpretable model can support the physicians' confidence in clinical decision-making.

An Innovative Solution Based on TSCA-ViT for Osteosarcoma Diagnosis in Resource-Limited Settings.

Identifying and managing osteosarcoma pose significant challenges, especially in resource-constrained developing nations. Advanced diagnostic methods involve isolating the nucleus from cancer cells for comprehensive analysis. However, two main challenges persist: mitigating image noise during the capture and transmission of cellular sections, and providing an efficient, accurate, and cost-effective solution for cell nucleus segmentation. To tackle these issues, we introduce the Twin-Self and Cross-Attention Vision Transformer (TSCA-ViT). This pioneering AI-based system employs a directed filtering algorithm for noise reduction and features an innovative transformer architecture with a twin attention mechanism for effective segmentation. The model also incorporates cross-attention-enabled skip connections to augment spatial information. We evaluated our method on a dataset of 1000 osteosarcoma pathology slide images from the Second People's Hospital of Huaihua, achieving a remarkable average precision of 97.7%. This performance surpasses traditional methodologies. Furthermore, TSCA-ViT offers enhanced computational efficiency owing to its fewer parameters, which results in reduced time and equipment costs. These findings underscore the superior efficacy and efficiency of TSCA-ViT, offering a promising approach for addressing the ongoing challenges in osteosarcoma diagnosis and treatment, particularly in settings with limited resources.

A Medically Assisted Model for Precise Segmentation of Osteosarcoma Nuclei on Pathological Images.

Osteosarcoma is the most common malignant bone tumor with a high degree of malignancy and misdiagnosis rates. Pathological images are crucial for its diagnosis. However, underdeveloped regions currently lack sufficient high-level pathologists, leading to uncertain diagnostic accuracy and efficiency. Existing research on pathological image segmentation often neglects the differences in staining styles and lack of data, without considering medical backgrounds. To alleviate the difficulty in diagnosing osteosarcoma in underdeveloped areas, an intelligent assisted diagnosis and treatment scheme for osteosarcoma pathological images, ENMViT, is proposed. ENMViT utilizes KIN to achieve normalization of mismatched images with limited GPU resources and uses traditional data enhancement methods, such as cleaning, cropping, mosaic, Laplacian sharpening, and other techniques to alleviate the issue of insufficient data. A multi-path semantic segmentation network combining Transformer and CNN is used to segment images, and the degree of edge offset in the spatial domain is introduced into the loss function. Finally, noise is filtered according to the size of the connecting domain. This article experimented on more than 2000 osteosarcoma pathological images from Central South University. The experimental results demonstrate that this scheme performs well in each stage of the osteosarcoma pathological image processing, and the segmentation results' IoU index is 9.4% higher than the comparative models, demonstrating its significant value in the medical industry.

Artificial Intelligence-Aided Diagnosis Solution by Enhancing the Edge Features of Medical Images.

Bone malignant tumors are metastatic and aggressive. The manual screening of medical images is time-consuming and laborious, and computer technology is now being introduced to aid in diagnosis. Due to a large amount of noise and blurred lesion edges in osteosarcoma MRI images, high-precision segmentation methods require large computational resources and are difficult to use in developing countries with limited conditions. Therefore, this study proposes an artificial intelligence-aided diagnosis scheme by enhancing image edge features. First, a threshold screening filter (TSF) was used to pre-screen the MRI images to filter redundant data. Then, a fast NLM algorithm was introduced for denoising. Finally, a segmentation method with edge enhancement (TBNet) was designed to segment the pre-processed images by fusing Transformer based on the UNet network. TBNet is based on skip-free connected U-Net and includes a channel-edge cross-fusion transformer and a segmentation method with a combined loss function. This solution optimizes diagnostic efficiency and solves the segmentation problem of blurred edges, providing more help and reference for doctors to diagnose osteosarcoma. The results based on more than 4000 osteosarcoma MRI images show that our proposed method has a good segmentation effect and performance, with Dice Similarity Coefficient (DSC) reaching 0.949, and show that other evaluation indexes such as Intersection of Union (IOU) and recall are better than other methods.

An Intelligent Auxiliary Framework for Bone Malignant Tumor Lesion Segmentation in Medical Image Analysis.

Bone malignant tumors are metastatic and aggressive, with poor treatment outcomes and prognosis. Rapid and accurate diagnosis is crucial for limb salvage and increasing the survival rate. There is a lack of research on deep learning to segment bone malignant tumor lesions in medical images with complex backgrounds and blurred boundaries. Therefore, we propose a new intelligent auxiliary framework for the medical image segmentation of bone malignant tumor lesions, which consists of a supervised edge-attention guidance segmentation network (SEAGNET). We design a boundary key points selection module to supervise the learning of edge attention in the model to retain fine-grained edge feature information. We precisely locate malignant tumors by instance segmentation networks while extracting feature maps of tumor lesions in medical images. The rich contextual-dependent information in the feature map is captured by mixed attention to better understand the uncertainty and ambiguity of the boundary, and edge attention learning is used to guide the segmentation network to focus on the fuzzy boundary of the tumor region. We implement extensive experiments on real-world medical data to validate our model. It validates the superiority of our method over the latest segmentation methods, achieving the best performance in terms of the Dice similarity coefficient (0.967), precision (0.968), and accuracy (0.996). The results prove the important contribution of the framework in assisting doctors to improve the accuracy of diagnosis and clinical efficiency.

Auxiliary Segmentation Method of Osteosarcoma MRI Image Based on Transformer and U-Net.

One of the most prevalent malignant bone tumors is osteosarcoma. The diagnosis and treatment cycle are long and the prognosis is poor. It takes a lot of time to manually identify osteosarcoma from osteosarcoma magnetic resonance imaging (MRI). Medical image processing technology has greatly alleviated the problems faced by medical diagnoses. However, MRI images of osteosarcoma are characterized by high noise and blurred edges. The complex features increase the difficulty of lesion area identification. Therefore, this study proposes an osteosarcoma MRI image segmentation method (OSTransnet) based on Transformer and U-net. This technique primarily addresses the issues of fuzzy tumor edge segmentation and overfitting brought on by data noise. First, we optimize the dataset by changing the precise spatial distribution of noise and the data-increment image rotation process. The tumor is then segmented based on the model of U-Net and Transformer with edge improvement. It compensates for the limitations of U-semantic Net by using channel-based transformers. Finally, we also add an edge enhancement module (BAB) and a combined loss function to improve the performance of edge segmentation. The method's accuracy and stability are demonstrated by the detection and training results based on more than 4,000 MRI images of osteosarcoma, which also demonstrate how well the method works as an adjunct to clinical diagnosis and treatment.

AI-Assisted Diagnosis and Decision-Making Method in Developing Countries for Osteosarcoma.

Osteosarcoma is a malignant tumor derived from primitive osteogenic mesenchymal cells, which is extremely harmful to the human body and has a high mortality rate. Early diagnosis and treatment of this disease is necessary to improve the survival rate of patients, and MRI is an effective tool for detecting osteosarcoma. However, due to the complex structure and variable location of osteosarcoma, cancer cells are highly heterogeneous and prone to aggregation and overlap, making it easy for doctors to inaccurately predict the area of the lesion. In addition, in developing countries lacking professional medical systems, doctors need to examine mass of osteosarcoma MRI images of patients, which is time-consuming and inefficient, and may result in misjudgment and omission. For the sake of reducing labor cost and improve detection efficiency, this paper proposes an Attention Condenser-based MRI image segmentation system for osteosarcoma (OMSAS), which can help physicians quickly locate the lesion area and achieve accurate segmentation of the osteosarcoma tumor region. Using the idea of AttendSeg, we constructed an Attention Condenser-based residual structure network (ACRNet), which greatly reduces the complexity of the structure and enables smaller hardware requirements while ensuring the accuracy of image segmentation. The model was tested on more than 4000 samples from two hospitals in China. The experimental results demonstrate that our model has higher efficiency, higher accuracy and lighter structure for osteosarcoma MRI image segmentation compared to other existing models.

A Multimodal Auxiliary Classification System for Osteosarcoma Histopathological Images Based on Deep Active Learning.

Histopathological examination is an important criterion in the clinical diagnosis of osteosarcoma. With the improvement of hardware technology and computing power, pathological image analysis systems based on artificial intelligence have been widely used. However, classifying numerous intricate pathology images by hand is a tiresome task for pathologists. The lack of labeling data makes the system costly and difficult to build. This study constructs a classification assistance system (OHIcsA) based on active learning (AL) and a generative adversarial network (GAN). The system initially uses a small, labeled training set to train the classifier. Then, the most informative samples from the unlabeled images are selected for expert annotation. To retrain the network, the final chosen images are added to the initial labeled dataset. Experiments on real datasets show that our proposed method achieves high classification performance with an AUC value of 0.995 and an accuracy value of 0.989 using a small amount of labeled data. It reduces the cost of building a medical system. Clinical diagnosis can be aided by the system's findings, which can also increase the effectiveness and verifiable accuracy of doctors.

Intelligent Assistant Diagnosis System of Osteosarcoma MRI Image Based on Transformer and Convolution in Developing Countries.

Osteosarcoma is a malignant bone tumor commonly found in adolescents or children, with high incidence and poor prognosis. Magnetic resonance imaging (MRI), which is the more common diagnostic method for osteosarcoma, has a very large number of output images with sparse valid data and may not be easily observed due to brightness and contrast problems, which in turn makes manual diagnosis of osteosarcoma MRI images difficult and increases the rate of misdiagnosis. Current image segmentation models for osteosarcoma mostly focus on convolution, whose segmentation performance is limited due to the neglect of global features. In this paper, we propose an intelligent assisted diagnosis system for osteosarcoma, which can reduce the burden of doctors in diagnosing osteosarcoma from three aspects. First, we construct a classification-image enhancement module consisting of resnet18 and DeepUPE to remove redundant images and improve image clarity, which can facilitate doctors' observation. Then, we experimentally compare the performance of serial, parallel, and hybrid fusion transformer and convolution, and propose a Double U-shaped visual transformer with convolution (DUconViT) for automatic segmentation of osteosarcoma to assist doctors' diagnosis. This experiment utilizes more than 80,000 osteosarcoma MRI images from three hospitals in China. The results show that DUconViT can better segment osteosarcoma with DSC 2.6% and 1.8% higher than Unet and Unet++, respectively. Finally, we propose the pixel point quantification method to calculate the area of osteosarcoma, which provides more reference basis for doctors' diagnosis.

A Residual Fusion Network for Osteosarcoma MRI Image Segmentation in Developing Countries.

Among primary bone cancers, osteosarcoma is the most common, peaking between the ages of a child's rapid bone growth and adolescence. The diagnosis of osteosarcoma requires observing the radiological appearance of the infected bones. A common approach is MRI, but the manual diagnosis of MRI images is prone to observer bias and inaccuracy and is rather time consuming. The MRI images of osteosarcoma contain semantic messages in several different resolutions, which are often ignored by current segmentation techniques, leading to low generalizability and accuracy. In the meantime, the boundaries between osteosarcoma and bones or other tissues are sometimes too ambiguous to separate, making it a challenging job for inexperienced doctors to draw a line between them. In this paper, we propose using a multiscale residual fusion network to handle the MRI images. We placed a novel subnetwork after the encoders to exchange information between the feature maps of different resolutions, to fuse the information they contain. The outputs are then directed to both the decoders and a shape flow block, used for improving the spatial accuracy of the segmentation map. We tested over 80,000 osteosarcoma MRI images from the PET-CT center of a well-known hospital in China. Our approach can significantly improve the effectiveness of the semantic segmentation of osteosarcoma images. Our method has higher F1, DSC, and IOU compared with other models while maintaining the number of parameters and FLOPS.

Auxiliary Segmentation Method of Osteosarcoma in MRI Images Based on Denoising and Local Enhancement.

Osteosarcoma is a bone tumor which is malignant. There are many difficulties when doctors manually identify patients' MRI images to complete the diagnosis. The osteosarcoma in MRI images is very complex, making its recognition and segmentation resource-consuming. Automatic osteosarcoma area segmentation can solve these problems to a certain extent. However, existing studies usually fail to balance segmentation accuracy and efficiency. They are either sensitive to noise with low accuracy or time-consuming. So we propose an auxiliary segmentation method based on denoising and local enhancement. The method first optimizes the osteosarcoma images, including removing noise using the Edge Enhancement based Transformer for Medical Image Denoising (Eformer) and using a non-parameter method to localize and enhance the tumor region in MRI images. Osteosarcoma was then segmented by Deep Feature Aggregation for Real-Time Semantic Segmentation (DFANet). Our method achieves impressive segmentation accuracy. Moreover, it is efficient in both time and space. It can provide information about the location and extent of the osteosarcoma as a basis for further diagnosis.

BA-GCA Net: Boundary-Aware Grid Contextual Attention Net in Osteosarcoma MRI Image Segmentation.

Osteosarcoma is one of the most common bone tumors that occurs in adolescents. Doctors often use magnetic resonance imaging (MRI) through biosensors to diagnose and predict osteosarcoma. However, a number of osteosarcoma MRI images have the problem of the tumor shape boundary being vague, complex, or irregular, which causes doctors to encounter difficulties in diagnosis and also makes some deep learning methods lose segmentation details as well as fail to locate the region of the osteosarcoma. In this article, we propose a novel boundary-aware grid contextual attention net (BA-GCA Net) to solve the problem of insufficient accuracy in osteosarcoma MRI image segmentation. First, a novel grid contextual attention (GCA) is designed to better capture the texture details of the tumor area. Then the statistical texture learning block (STLB) and the spatial transformer block (STB) are integrated into the network to improve its ability to extract statistical texture features and locate tumor areas. Over 80,000 MRI images of osteosarcoma from the Second Xiangya Hospital are adopted as a dataset for training, testing, and ablation studies. Results show that our proposed method achieves higher segmentation accuracy than existing methods with only a slight increase in the number of parameters and computational complexity.

A Cascaded Multi-Stage Framework for Automatic Detection and Segmentation of Pulmonary Nodules in Developing Countries.

Lung cancer has the highest mortality rate among all malignancies. Non-micro pulmonary nodules are the primary manifestation of early-stage lung cancer. If patients can be detected with nodules in the early stage and receive timely treatment, their survival rate can be improved. Due to the large number of patients and limited medical resources, doctors take a longer time to make a diagnosis, which reduces efficiency and accuracy. Besides, there are no suitable approaches for developing countries. Therefore, we propose a 2.5D-based cascaded multi-stage framework for automatic detection and segmentation (DS-CMSF) of pulmonary nodules. The first three stages of the framework are used to discover lesions, and the latter stage is used to segment them. The first locating stage introduces the classical 2D-based Yolov5 model to locate the nodules roughly on axial slices. The second aggregation stage proposes a candidate nodule selection (CNS) algorithm to locate further and reduce redundant candidate nodules. The third classification stage uses a multi-size 3D-based fusion model to accommodate nodules of varying sizes and shapes for false-positive reducing. The last segmentation stage introduces multi-scale and attention modules into 3D-based UNet autoencoder to segment the nodular regions finely. Our proposed framework achieves 95.95% sensitivity and 89.50% CPM for nodules detection on the LUNA16 dataset, and 86.75% DSC for nodules segmentation on the LIDC-IDRI dataset. Moreover, our approach also achieves the accuracy-complexity trade-off, which can effectively realize the auxiliary diagnosis of pulmonary nodules in developing countries.

An Artificial Intelligence Multiprocessing Scheme for the Diagnosis of Osteosarcoma MRI Images.

Osteosarcoma is the most common malignant osteosarcoma, and most developing countries face great challenges in the diagnosis due to the lack of medical resources. Magnetic resonance imaging (MRI) has always been an important tool for the detection of osteosarcoma, but it is a time-consuming and labor-intensive task for doctors to manually identify MRI images. It is highly subjective and prone to misdiagnosis. Existing computer-aided diagnosis methods of osteosarcoma MRI images focus only on accuracy, ignoring the lack of computing resources in developing countries. In addition, the large amount of redundant and noisy data generated during imaging should also be considered. To alleviate the inefficiency of osteosarcoma diagnosis faced by developing countries, this paper proposed an artificial intelligence multiprocessing scheme for pre-screening, noise reduction, and segmentation of osteosarcoma MRI images. For pre-screening, we propose the Slide Block Filter to remove useless images. Next, we introduced a fast non-local means algorithm using integral images to denoise noisy images. We then segmented the filtered and denoised MRI images using a U-shaped network (ETUNet) embedded with a transformer layer, which enhances the functionality and robustness of the traditional U-shaped architecture. Finally, we further optimized the segmented tumor boundaries using conditional random fields. This paper conducted experiments on more than 70,000 MRI images of osteosarcoma from three hospitals in China. The experimental results show that our proposed methods have good results and better performance in pre-screening, noise reduction, and segmentation.

Rethinking U-Net from an Attention Perspective with Transformers for Osteosarcoma MRI Image Segmentation.

Osteosarcoma is one of the most common primary malignancies of bone in the pediatric and adolescent populations. The morphology and size of osteosarcoma MRI images often show great variability and randomness with different patients. In developing countries, with large populations and lack of medical resources, it is difficult to effectively address the difficulties of early diagnosis of osteosarcoma with limited physician manpower alone. In addition, with the proposal of precision medicine, existing MRI image segmentation models for osteosarcoma face the challenges of insufficient segmentation accuracy and high resource consumption. Inspired by transformer's self-attention mechanism, this paper proposes a lightweight osteosarcoma image segmentation architecture, UATransNet, by adding a multilevel guided self-aware attention module (MGAM) to the encoder-decoder architecture of U-Net. We successively perform dataset classification optimization and remove MRI image irrelevant background. Then, UATransNet is designed with transformer self-attention component (TSAC) and global context aggregation component (GCAC) at the bottom of the encoder-decoder architecture to perform integration of local features and global dependencies and aggregation of contexts to learned features. In addition, we apply dense residual learning to the convolution module and combined with multiscale jump connections, to improve the feature extraction capability. In this paper, we experimentally evaluate more than 80,000 osteosarcoma MRI images and show that our UATransNet yields more accurate segmentation performance. The IOU and DSC values of osteosarcoma are 0.922 ± 0.03 and 0.921 ± 0.04, respectively, and provide intuitive and accurate efficient decision information support for physicians.

Intelligent Segmentation Medical Assistance System for MRI Images of Osteosarcoma in Developing Countries.

Osteosarcoma is the most common primary malignant bone tumor in children and adolescents. It has a high degree of malignancy and a poor prognosis in developing countries. The doctor manually explained that magnetic resonance imaging (MRI) suffers from subjectivity and fatigue limitations. In addition, the structure, shape, and position of osteosarcoma are complicated, and there is a lot of noise in MRI images. Directly inputting the original data set into the automatic segmentation system will bring noise and cause the model's segmentation accuracy to decrease. Therefore, this paper proposes an osteosarcoma MRI image segmentation system based on a deep convolution neural network, which solves the overfitting problem caused by noisy data and improves the generalization performance of the model. Firstly, we use Mean Teacher to optimize the data set. The noise data is put into the second round of training of the model to improve the robustness of the model. Then, we segment the image using a deep separable U-shaped network (SepUNet) and conditional random field (CRF). SepUnet can segment lesion regions of different sizes at multiple scales; CRF further optimizes the boundary. Finally, this article calculates the area of the tumor area, which provides a more intuitive reference for assisting doctors in diagnosis. More than 80000 MRI images of osteosarcoma from three hospitals in China were tested. The results show that the proposed method guarantees the balance of speed, accuracy, and cost under the premise of improving accuracy.

Disease Control and Prevention in Rare Plants Based on the Dominant Population Selection Method in Opportunistic Social Networks.

The spread of seeds of rare and dangerous plants affects the regeneration, pattern, genetic structure, invasion, and settlement of plant populations. However, seed transmission is a relatively weak research link. The spread of plant seeds is not controlled by the communicator. Rather, this event results from the interaction between the host and the external environment determined by the mother. The way plants transmit and accept seeds is similar to how user nodes accept data transmission requests in social networks. Plants select the characteristics including seed size, maturity time, and gene matching, which are consistent with the size, delay, and keywords of the data received by the user. In this study, we selected rare and endangered Pterospermum heterophyllum as the research object and applied them to a social network. All plants were considered nodes and all seeds as transmitted data. This method avoids the influence of errors in actual sampling and statistical laws. By using historical information to record the reception of seeds, the Infection and Immunity Algorithm (IAIA) in opportunistic social networks was established. This method selects healthy plants through plant social populations and reduces the number of diseased plants. The experimental results show that the IAIA algorithm has a good effect in distinguishing dominant seedlings from seedlings with disease genes and realizes the selection of dominant plants in social networks.

A Convolutional Neural Network-Based Intelligent Medical System with Sensors for Assistive Diagnosis and Decision-Making in Non-Small Cell Lung Cancer.

In many regions of the world, early diagnosis of non-small cell lung cancer (NSCLC) is a major challenge due to the large population and lack of medical resources, which is difficult toeffectively address via limited physician manpower alone. Therefore, we developed a convolutional neural network (CNN)-based assisted diagnosis and decision-making intelligent medical system with sensors. This system analyzes NSCLC patients' medical records using sensors to assist staging a diagnosis and provides recommended treatment plans to physicians. To address the problem of unbalanced case samples across pathological stages, we used transfer learning and dynamic sampling techniques to reconstruct and iteratively train the model to improve the accuracy of the prediction system. In this paper, all data for training and testing the system were obtained from the medical records of 2,789,675 patients with NSCLC, which were recorded in three hospitals in China over a five-year period. When the number of case samples reached 8000, the system achieved an accuracy rate of 0.84, which is already close to that of the doctors (accuracy: 0.86). The experimental results proved that the system can quickly and accurately analyze patient data and provide decision information support for physicians.