OPTIMISING CLASSIFICATION TECHNIQUES FOR LUNG CANCER DETECTION ON CT IMAGES

Lung cancer remains one of the deadliest cancers worldwide, with survival rates significantly dependent on early detection. Traditional diagnostic methods, while effective, often identify lung cancer in advanced stages, limiting treatment options. Recently, artificial intelligence (AI) and deep learning (DL) models have gained attention for their potential to assist in early lung cancer diagnosis through radiological data, particularly computed tomography (CT) and X-ray imaging. This review examines recent advancements in AI and DL models specifically applied to early lung cancer detection, offering a comprehensive look at model architecture, data preprocessing techniques, and performance metrics. AI models, particularly those using convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have shown significant promise in improving diagnostic accuracy. In studies published over the past two years, CNN-based models have achieved sensitivity rates upwards of 90% when applied to large datasets, such as the LIDC-IDRI, a publicly available database containing thousands of annotated lung scans. Furthermore, hybrid approaches combining AI models with radiologist expertise have demonstrated reduced false positives, a frequent challenge in automated image analysis. A critical factor in these advancements has been data quality and volume. Large, annotated datasets with high-resolution images have enabled more robust training and validation, essential for refining these models. However, challenges remain, particularly regarding the standardization of data across institutions, variations in scanner quality, and ethical concerns surrounding patient privacy. The review also highlights studies exploring multi-modal approaches, integrating radiological data with clinical records and genetic markers to create more personalized diagnostic tools. These multi-modal methods have the potential to improve predictive accuracy further, though they currently require more extensive validation. Overall, while AI and DL technologies offer transformative potential for early lung cancer detection, widespread implementation depends on continued research in model accuracy, data standardization, and ethical safeguards. This paper concludes by emphasizing the need for collaboration across disciplines—AI researchers, radiologists, and oncologists—to refine these models into reliable tools that can be integrated seamlessly into clinical workflows for timely and accurate lung cancer diagnosis.

Detection of metastasis of mediastinal lymph nodes in lung cancer patients with an artificial intelligence model.

The early detection, early diagnosis, and early treatment of lung cancer are the best strategies to improve the 5-year survival rate. Logistic regression analysis can be a helpful tool in the early detection of high-risk groups of lung cancer. Convolutional neural network (CNN) could distinguish benign from malignant pulmonary nodules, which is critical for early precise diagnosis and treatment. Here, we developed a risk assessment model of lung cancer and a high-precision classification diagnostic model using these technologies so as to provide a basis for early screening of lung cancer and for intelligent differential diagnosis. A total of 355 lung cancer patients, 444 patients with benign lung disease and 472 healthy people from The First Affiliated Hospital of Zhengzhou University were included in this study. Moreover, the dataset of 607 lung computed tomography images was collected from the above patients. The logistic regression method was employed to screen the high-risk groups of lung cancer, and the CNN model was designed to classify pulmonary nodules into benign or malignant nodules. The area under the curve of the lung cancer risk assessment model in the training set and the testing set were 0.823 and 0.710, respectively. After finely optimizing the settings of the CNN, the area under the curve could reach 0.984. This performance demonstrated that the lung cancer risk assessment model could be used to screen for high-risk individuals with lung cancer and the CNN framework was suitable for the differential diagnosis of pulmonary nodules.

Lung cancer is one of the most prevalent and lethal malignant tumors globally, characterized by high morbidity and mortality rates. Early diagnosis is crucial for enhancing treatment efficacy, reducing mortality, and improving patients’ quality of life. Traditional manual interpretation of computed tomography (CT) images—the primary screening tool for lung cancer—is often limited by subjectivity, heavy workload, and potential missed diagnoses. Artificial Intelligence (AI) methodologies have emerged as indispensable assets for radiologists, empowering them to conduct in-depth analyses of lung CT scans. This technology facilitates a more streamlined and unbiased evaluation of anatomical structures and pathological features depicted in chest CT images. Driven by advances in deep learning models such as convolutional neural networks (CNNs), the accuracy and robustness of AI methods for lung cancer diagnosis have been significantly improved, providing reliable references for clinical practice. This paper presents a brief review of the applications of AI methods in lung cancer diagnosis based on CT imaging in recent years. It systematically organizes and elaborates on the applications of AI methods represented by deep learning in pulmonary nodule detection, lung tumor segmentation, benign-malignant classification of lung cancer, and lung cancer-related gene mutation detection. Additionally, it analyzes the performance of different models on relevant datasets. Furthermore, the limitations of AI in CT imaging-based lung cancer diagnosis and future research directions are summarized. Through this survey, we aim to provide a concise and comprehensive overview of the current status of lung cancer diagnosis using CT-AI integrated systems, and ultimately promote the development and clinical application of these technologies in the early and accurate detection of lung cancer.

Currently, digital healthcare technologies based on Internet of Things (IOT) are emerging, and the market for digital healthcare was estimated to be 151.5 billion dollars by 2020. Among the digital healthcare technologies, disease diagnosis is attracting maximum attention. Traditional diagnostic methods, such as radiology and human biological material analysis, have limitations of long study time, high cost, radiation exposure, and pain. Therefore, non-invasive diagnostics methods, for early diagnosis of specific diseases and personal healthcare management, have attracted attention.Breath sensors are one of the promising candidates for the realization of non-invasive diagnosis owing to their ease of use, safety, and cost effectiveness. The exhaled breath analysis technique holds great promise as a non-invasive method for diagnosing lung cancer. In the breath of lung cancer patients, various volatile organic compounds (VOCs) markers are generated during metabolic processes. Analyzing these VOCs markers presents a compelling avenue for early and non-invasive lung cancer diagnosis. Solid state gas sensors capable of measuring specific VOCs are currently being researched and utilized as commercial gas sensors for this purpose. However, the concentration of lung cancer VOCs markers in exhaled breath is commonly very low, and individual commercial gas sensors often lack the necessary sensitivity and selectivity to accurately diagnose lung cancer. In this study, we propose a multimodal solid state electronic biosensor system designed to differentiate lung cancer VOCs characteristics among multiple VOCs, enabling exhalation-based lung cancer diagnosis. Initially, we developed multimodal biosensors using more than 20 gas sensors, consisting of metal oxide semiconductor sensors, electrochemical sensors, photoionized detectors, metalorganic frame sensors and so on. And each exhibiting varying sensitivity profiles for complex gas mixtures. Additionally, we designed a classification algorithm based on convolutional neural networks (CNN) and multi-layered perceptron (MLP) to distinguish between lung cancer patients and healthy individuals using the output from these diverse multimodal gas sensors. To evaluate the system's performance, we collected exhaled air samples from 74 lung cancer patients and 107 normal subjects, conducting clinical trials with our developed system. We did model evaluation with confusion matrix and ROC curve. The results of these tests confirmed that our system achieved a high predictive accuracy of over 95% in classifying lung cancer patients. The simplicity of the sensor configuration in our developed system makes it suitable for large-scale screening tests to identify potential candidates for precise lung cancer diagnosis. This technology has the potential to contribute to reduced national welfare costs and improved patient survival rates through early diagnosis.

Lung cancer is one of the leading diseases that cause death in the world. Early diagnosis of lung cancer is as important as its treatment. Therefore, we propose the LinkNet architecture, which is a deep learning model that will detect the location and size of nodules from the lung tomography image. The study was conducted with 110 patients and 343 nodules with nodules detected in Lung Computed Tomography (CT) Images. In the study, no public dataset was used and tomography images were obtained from the hospital. In the pre-processing stage, thresholding is made according to the lung Hounsfield Unit (HU) threshold value with the Otsu method and the lung is segmented. The XML (Extensible Markup Language) files of ROIs(Region of Interest) of the nodules previously marked by the radiologist are extracted and converted into images. Using template nodules trained with LinkNet and U-Net architectures, comparison and success percentage tables for 16 different architectures were presented. Using LinkNet, the model achieved an intersection of union (IoU) score of 69% for valid and an IoU score of 94% for the train. 274 nodule data were used in the train section and 69 nodule data were used in the validation section. Experimental results show that nodules that may be overlooked by a radiologist can be detected with CAD (Computer- Aided Design) performed and will be useful in the diagnosis of lung cancer.

Advanced vapour sensing materials: Existing and latent to acoustic wave sensors for VOCs detection as the potential exhaled breath biomarkers for lung cancer

Microsatellite alterations in bronchial and sputum specimens of lung cancer patients: Miozzo M, Sozzi G, Musso K et al. Division of Experimental Oncology A, Istituto Nazionale Tumori, Via G, Venezian 1, 20133 Milano. Cancer Res 1996;56:2285–2288

Multi-center study on the application potential of Siaα-2,6Gal in early and differential diagnosis of lung cancer.

Lung cancer is the most important leading cause of cancer death. In the United States alone, 169,000 new cases were identified in 2002 (1) and lung cancer is responsible for more than 1 million deaths worldwide (2). Lung cancer has a lower incidence than other types of cancer (breast, colon, prostate, and cervical) but is more lethal. Throughout most of the world, 5-yr survival is less than 10%. Even with smoking cessation, the risk of lung cancer remains elevated because the injury in the airway tissue cannot be reversed (3). One of the reasons for such high mortality is the late detection of lung cancer, which in most cases occurs after metastatic dissemination. In those cases, the benefits of chemotherapy are modest. The classical diagnostic imaging technology, chest X-ray, is not sensitive enough to provide a reliable early diagnosis of lung cancer. One approach to improve early detection is looking for specific molecular markers that may be tools for a better understanding of the initial steps of carcinogenesis.

SummaryLung cancer has become one of the major diseases that seriously threaten human health. Early diagnosis of lung cancer is very important for the quality of lung cancer patients. Researching computer‐assisted systems for lung cancer recognition is one of the effective means to help physicians quickly diagnose lung cancer. Therefore, this paper studies the multimodal recognition algorithm for lung images. This algorithm uses CT images, PET images, and PET/CT images of lung tumors as the experimental objects and uses convolutional neural network to realize lung tumor recognition. Based on the recognition of lung tumors by convolutional neural networks, an integrated convolutional neural network was used to identify lung tumors to improve the recognition accuracy and reduce the training time. The experimental results show that the convolutional neural network can effectively identify the lung tumor images. The number of iterations and batch size in the training process will have an impact on the recognition of lung tumors. The integrated convolutional neural network designed is superior to single convolutional neural network in terms of recognition accuracy and time consumption.

BackgroundTo distinguish early‐stage lung cancer from benign disease in pulmonary nodules, especially lesions with ground‐glass opacity (GGO), we assessed gene mutations of ctDNA in peripheral blood using targeted next‐generation sequencing (NGS).MethodsSingle pulmonary nodule patients without mediastinal lymph nodes and symptoms that were hard to diagnose by chest CT and lung cancer biomarker measurement in multiple medical centers were enrolled into the study. All patients accepted minimally invasive surgery but refused preoperative biopsy. Gene mutations in preoperative blood samples were detected by targeted NGS. Mutations with significant differences between lung tumors and benign lesions, as grouped by postoperative pathology, were screened. Protein expression was determined by immunohistochemistry. Highly expressed genes were selected as biomarkers to verify the mutations in peripheral blood.ResultsIn the training set, the RNF213, KMT2D, CSMD3 and LRP1B genes were mutated more frequently in early‐stage lung cancer (27 cases) than in benign nodules (15 cases) (P < 0.05). High expression of the RNF213 gene in lung cancers and low expression in benign diseases were seen by immunohistochemistry. The RNF213 gene was mutated in 25% of lung cancer samples in the validation set of 28 samples and showed high specificity (100%). In GGO patients, RNF213 was mutated more frequently in early‐stage lung cancer compared to benign diseases (P < 0.05).Conclusions RNF213 gene mutations were observed more frequently in early‐stage lung cancer, but not in benign nodules. Mutation of the RNF213 gene in peripheral blood may be a high specificity biomarker for the assisted early diagnosis of lung cancer in pulmonary nodules.Key pointsSignificant findings of the study: In peripheral venous blood and tumor tissue, RNF213 gene mutated more frequently in lung cancer than benign pulmonary nodules.What this study adds: Detection mutation of the RNF213 gene in peripheral blood may be a high specificity method for the assisted early diagnosis of lung cancer in pulmonary nodules.

CCSA-RF enhanced lightweight multi-scale CNN framework for robust lung cancer classification

Lung cancer is the result of cells that develop widely in one or both of the lungs. Lung cancer is diagnosed in two ways non-small cell lung cancer and small cell lung cancer. Too late, lung cancer has been the main cause of death worldwide. If detected successfully in the early stages lung cancer allows for many treatment options, decrease the risk of aggressive surgery an increased rate of survival. So the early diagnosis of lung cancer is very important. Our aims and objectives to develop this system is to detect the lung cancer in an input image and shows that image of lung is normal or abnor0mal. Here we proposed a system where radiologist login with system by entering his/her email and password. Radiologist fill patient form with coordination of patient and upload CT scan image of lungs in predictive model for lung cancer detection and get output and print report .Several image processing techniques that we have used to design this system, Image Pre-processing, Image Segmentation, Image Filtering, Dilation, Image Filling, Feature Extraction Neural Network and Neuro Fuzzy classification algorithm is used for image classification. MATLAB is used to designed detection part of system.

Deep learning based on lung cancer classification has been used increasingly for the early diagnosis for several reasons such as lack of robust deep learning-based system, complexity of nodule structure, lack of proper lung segmentation technique, high false positive result, lack of best feature extraction and less amount of medical imaging data for training deep learning model, it has been difficult to get high classification performance. The aim of this paper getting high lung cancer classification performance. We introduce the Data, Classification technique and View (DCV) as main components of the system that concern for the better lung cancer classification results, along with them different intermediate components such as Lung nodule segmentation, Feature extraction, Feature reduction are also defined. These components are key for providing better classification performance result which helps radiologist for early diagnosis of lung cancer. We have proposed uses image data having different dimensionality as input to the deep learning based classifier which provides lung cancer classification to be viewed by radiologists for the early diagnosis of lung cancer.We evaluated the proposed DCV system by classifying 30 state-of-art research papers in the field of deep learning based lung cancer classification system. Through this paper, readers will get the result of deep learning based lung cancer classification system. Also, readers will understand the classification groups, validation criteria, future gaps of the 30 literature.