Improving lung cancer detection using faster region‐based convolutional neural network aided with fuzzy butterfly optimization algorithm

Lung cancer is the most dangerous of all forms of cancer and it has the highest occurrence rate, world over. Early detection of lung cancer is a difficult task. Medical images generated by computer tomography (CT) are being used extensively for lung cancer analysis and research. However, it is essential to have a well-organized image database in order to design a reliable computer-aided diagnosis (CAD) tool. Identifying the most appropriate dataset for the research is another big challenge. The objective of this paper is to present a review of literature related to lung CT datasets. The Cancer Imaging Archive (TCIA) consortium collates different types of cancer datasets and permits public access through an integrated search engine. This survey summarizes the research work done using lung CT datasets maintained by TCIA. The motivation to present this survey was to help the research community in selecting the right lung dataset and to provide a comprehensive summary of the research developments in the field.

Early detection of lung cancer greatly improves survival rates. However, it is still the leading cause of cancer-related deaths around the world. Radiologists often spend a lot of time on assessments, and these can sometimes be inconsistent when they diagnose from CT images. With the increasing availability of deep learning, especially convolutional neural networks (CNNs), automated tools present a promising way to improve diagnostic speed and reliability. This study looks at the performance of four pre-trained CNN architectures: DenseNet-169, EfficientNet-B7, Inception-V3, and ResNet-152. These networks help classify cancerous and non-cancerous lung tissue from CT scans. This project used a dataset of 5,000 images from The Cancer Imaging Archive (TCIA). To address the limited number of malignant cases, balanced the classes and used data augmentation. After fine-tuning, DenseNet-169 achieved the highest accuracy at 95.6%, followed by EfficientNet-B7, Inception-V3, and ResNet-152, with their respective accuracies noted. These findings show that CNN-based models can provide high diagnostic accuracy. They also offer a steady, fast alternative to manual image reading, making them good candidates for use in clinical settings, especially where resources are limited.

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.

Recent developments in deep learning techniques have gained significant attention in medical image analysis. Deep learning techniques have been shown to give promising results for automating various medical image tasks like segmentation of organs, precise delineation of lesions and automated disease diagnosis.We have demonstrated the utility of deep learning models for finding associations between brain imaging phenotypes and the molecular subtype. In this study Magnetic Resonance (MR) images of the brain with Glioblastoma multiforme (GBM) were used. The Cancer Genome Atlas (TCGA) has grouped GBM into four distinct subtypes, namely - Mesenchymal, Neural, Proneural and Classical. The subtype classification are defined by genomic characteristics, survival outcomes, patient age and response to treatment. Identification of molecular subtype and its associated imaging phenotype could aid in developing precision medicine and personalized treatments for patients.The MR imaging data and molecular subtype information were retrospectively obtained from The Cancer Imaging Archive (TCIA) of patients with high-grade gliomas. From the TCIA, 123 patient cases were manually identified which had the following four MR sequences- a) T1 and b) post-contrast T1-weighted (T1c), c) T2-weighted (T2), and d) T2 Fluid Attenuated Inversion Recovery (FLAIR). The MR dataset was further split into 92 and 31 cases for training and testing. The pre-processing of MR images involved skull-stripping, co-registration of MR sequences to T1c, re-sampling of MR volumes to isotropic voxels and segmentation of brain lesion. The lesions in the MR volumes were automatically segmented using a trained convolutional Neural Network (CNN) on BraTS2017 segmentation challenge dataset. From the segmentation maps 64×64×64 cube patches centered around the tumor were extracted from all the four MR sequences and a 3D convolutional neural network was trained for the molecular subtype classification. On the held-out test set, our approach achieved a classification accuracy of 90%. These results on TCIA dataset highlight the emerging role of deep learning in understanding molecular markers from non-invasive imaging phenotypes.

Radiomics Prediction of Radiation Treatment Outcomes in Oropharyngeal Cancer: A Clinical and Image Repository in Concert with the Cancer Imaging Archive (TCIA)

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.

The aim of this study was to evaluate the performance of a clinical decision support system (CDSS) for therapeutic plans in geriatric dentistry. The information that needs to be considered in a therapeutic plan includes not only the patient's oral health status obtained from an oral examination, but also other related factors such as underlying diseases, socioeconomic characteristics, and functional dependency. A Bayesian network (BN) was used as a framework to construct a model of contributing factors and their causal relationships based on clinical knowledge and data. The faster R-CNN (regional convolutional neural network) algorithm was used to detect oral health status, which was part of the BN structure. The study was conducted using retrospective data from 400 patients receiving geriatric dental care at a university hospital between January 2020 and June 2021. The model showed an F1-score of 89.31%, precision of 86.69%, and recall of 82.14% for the detection of periodontally compromised teeth. A receiver operating characteristic curve analysis showed that the BN model was highly accurate for recommending therapeutic plans (area under the curve = 0.902). The model performance was compared to that of experts in geriatric dentistry, and the experts and the system strongly agreed on the recommended therapeutic plans (kappa value = 0.905). This research was the first phase of the development of a CDSS to recommend geriatric dental treatment. The proposed system, when integrated into the clinical workflow, is expected to provide general practitioners with expert-level decision support in geriatric dental care.

Bus passengers' data are crucial for the bus agents, especially in Indonesia. With this data, bus agents could identify the traffic for each route of the bus. To handle this problem, many researchers have made a system to count and detect the public transportation passengers with different algorithms. Many researchers defined that You Look Only Once (YOLO) has best performance to overcome the object detection problem that has similarity in this research. The Convolutional Neural Network algorithm is also not inferior in implementing object detection either. In this research, it will investigate these three algorithms, You Only Look Once version 8 (YOLOv8), Faster Region Convolutional Neural Network (Faster R-CNN), and Mask Region Convolutional Neural Network (Mask R-CNN), in counting bus passengers and detecting the bus passenger’s gender. To find the best performance of these two algorithms, they will use a dataset that contains 408 photos of bus passengers. This research aims to analyze the result of the bus passengers data that could reduce the misalignment and determine the best algorithm to use in this case.

Lung cancer diagnosis and prediction in the earliest stage of the disease can be extremely helpful to improve the survival rate of patients, but diagnosing cancer is one of the most challenging tasks for a radiologist. In this paper, we propose that convolutional neural networks (CNNs) can effectively detect and predict lung cancer. The design of model has 8 layers in which one is input layer and two conv layers and a ReLU, max-pooling to extract features and a fully connected layer which has sigmoid activation function which in terms connected to 1 neuron which is an output layer. This ouput layer is responsible for classifying the image in to a benign or malignant nodules. We have acquired the dataset from Kaggle having 1097 cases which can be passed as input data for input layer. A 2D set of resampled images with 64 * 64 pixels of size which are rotated, scaled, and randomly translated images is generated as input samples.

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.

Electrochemical biosensors for the detection of lung cancer biomarkers: A review

The incidence and mortality rates of lung cancer have been increasing in many countries. However, the true effectiveness of screening for lung cancer is still controversial. This study aimed to examine the growth pattern of lung cancer and to evaluate the efficacy of screening. The authors linked the records of annual radiologic screening to cancer registry data and conducted a retrospective follow-up study of radiographs in all patients with lung cancer arising in a population undergoing screening. Among a total of 305,934 participants, screening detected 206 lung cancers, 103 of which were Stage I disease. Seventy-one of the 131 adenocarcinomas were Stage I, and 58% of them showed evidence of cancer for 2 years on a retrospective review of radiographs. Presentation as small faint lesions overlapping the normal chest structures delayed the early detection of adenocarcinoma. The overall sensitivity of screening was 70%, 52% for squamous cell carcinoma and 50% for small cell carcinoma. Rapidly growing Stage II-IV tumors without retrospective evidence of cancer on previous radiographs accounted for most of the cancers detected during the intervals between screening. Both the low detectability of Stage I adenocarcinoma and the late recognition of rapidly growing small cell and squamous cell carcinomas reduced the effectiveness of screening. More effective imaging methods and an antismoking campaign are required to reduce lung cancer mortality.

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.

A review of detection methods for the early diagnosis of lung cancer

Lung cancer is the first killer among the cancer deaths. Malignant lung nodules have extremely high mortality while some of the benign nodules don't need any treatment .Thus, the accuracy of diagnosis between benign or malignant nodules diagnosis is necessary. Notably, although currently additional invasive biopsy or second CT scan in 3 months later may help radiologists to make judgments, easier diagnosis approaches are imminently needed. In this paper, we propose a novel CAD method to distinguish the benign and malignant lung cancer from CT images directly, which can not only improve the efficiency of rumor diagnosis but also greatly decrease the pain and risk of patients in biopsy collecting process. Briefly, according to the state-of-the-art radiomics approach, 583 features were used at the first step for measurement of nodules' intensity, shape, heterogeneity and information in multi-frequencies. Further, with Random Forest method, we distinguish the benign nodules from malignant nodules by analyzing all these features. Notably, our proposed scheme was tested on all 79 CT scans with diagnosis data available in The Cancer Imaging Archive (TCIA) which contain 127 nodules and each nodule is annotated by at least one of four radiologists participating in the project. Satisfactorily, this method achieved 82.7% accuracy in classification of malignant primary lung nodules and benign nodules. We believe it would bring much value for routine lung cancer diagnosis in CT imaging and provide improvement in decision-support with much lower cost.