Diagnosis Of Lung Cancer Research Articles

Family resilience and vulnerability of patients at diagnosis of lung cancer: A qualitative study.

To explore and analyse the adaptation process of patients and their families at the point of lung cancer diagnosis. Totally 23 operable lung cancer patients were included in this study. Colaizzi's method of phenomenology was employed for data analysis. This study found two different aspects of family adaptation at the diagnosis of lung cancer. For family resilience, three themes emerged: (1) Positive family belief systems (giving meaning to a cancer diagnosis and maintaining a positive/optimistic attitude), (2) Flexible family organizational patterns (maintaining the stability of family structure and function, adjusting the relationship between patients and family members and receiving external support and help) and (3) Good communication and problem-solving strategies (open communication on an equal basis, positive and open expression of emotions and collaborative problem-solving). For family vulnerability, three themes were as follows: (1) Negative family belief systems (negative attitudes and concealment and self-isolation due to stigma), (2) Rigid family organizational patterns (adaptation lost, conflicts between family support and patients' willingness and pressure upon social support) and (3) Unhealthy communication and problem-solving (poor communication, emotional asymmetry of family members and tendency to solve problems alone). The study highlights the existence of the family resilience and family vulnerability at the point of lung cancer diagnosis and provides patient's perspective for understanding family resilience in specific cultural contexts. The data were collected through face-to-face interviews. ChiCTR2300074801.

Healthcare disparities, screening, and molecular testing in the changing landscape of non-small cell lung cancer in the United States: a review.

Inequitable access to care continues to hinder improvements in diagnosis and treatment of lung cancer. This review describes healthcare disparities in the changing landscape of non-small cell lung cancer (NSCLC) in the United States, focusing on racial, ethnic, sex-based, and socioeconomic trends. Furthermore, strategies to address disparities, overcome challenges, and improve patient outcomes are proposed. Barriers exist across lung cancer screening, diagnosis, and treatment regimens, varying by sex, age, race and ethnicity, geography, and socioeconomic status. Incidence and mortality rates of lung cancer are higher among Black men than White men, and incidences in young women are substantially greater than in young men. Disparities may be attributed to geographic differences in screening access, with correlating higher incidence and mortality rates in rural versus urban areas. Lower socioeconomic status is also linked to lower survival rates. Several strategies could help reduce disparities and improve outcomes. Current guidelines could improve screening eligibility by incorporating sex, race, and socioeconomic status variables. Patient and clinician education on screening guidelines and patient-level barriers to care are key, and biomarker testing is critical since ~ 70% of patients with NSCLC have an actionable biomarker. Timely diagnosis, staging, and comprehensive biomarker testing, including cell-free DNA liquid biopsy, may provide valuable treatment guidance for patients with NSCLC. Efforts to improve lung cancer screening and biomarker testing access, decrease bias, and improve education about screening and testing are needed to reduce healthcare disparities in NSCLC.

Dysregulation of lncRNA MALAT1 Contributes to Lung Cancer in African Americans by Modulating the Tumor Immune Microenvironment

African American (AA) populations present with notably higher incidence and mortality rates from lung cancer in comparison to other racial groups. Here, we elucidated the contribution of long non-coding RNAs (lncRNAs) in the racial disparities and their potential clinical applications in both diagnosis and therapeutic strategies. AA patients had elevated plasma levels of MALAT1 and PVT1 compared with cancer-free smokers. Incorporating these lncRNAs as plasma biomarkers, along with smoking history, achieved 81% accuracy in diagnosis of lung cancer in AA patients. We observed a rise in MALAT1 expression, correlating with increased levels of monocyte chemoattractant protein-1 (MCP-1) and CD68, CD163, CD206, indicative of tumor-associated macrophages in lung tumors of AA patients. Forced MALAT1 expression led to enhanced growth and invasiveness of lung cancer cells, both in vitro and in vivo, accompanied by elevated levels of MCP-1, CD68, CD163, CD206, and KI67. Mechanistically, MALAT1 acted as a competing endogenous RNA to directly interact with miR-206, subsequently affecting MCP-1 expression and macrophage activity, and enhanced the tumorigenesis. Targeting MALAT1 significantly reduced tumor sizes in animal models. Therefore, dysregulated MALAT1 contributes to lung cancer disparities in AAs by modulating the tumor immune microenvironment through its interaction with miR-206, thereby presenting novel diagnostic and therapeutic targets.

Use of the Archimedes navigation system to diagnose peripheral pulmonary lesions: preliminary Italian results

Diagnosis of peripheral pulmonary lesions (PPL) is one of the most challenging fields in early lung cancer diagnosis. Despite novel techniques and new approaches to the periphery of the lung, almost 25% of PPL remains undiagnosed. Virtual bronchoscopy navigation (VBN) potentially allows to sample PPL previously not reachable with conventional bronchoscopy. In this preliminary report, we described nine cases of PPL (in which conventional bronchoscopy did not reach the lesion) sampled with VBN, from which we obtained a diagnosis in seven out of nine cases (77.8%), consistent with other reported results in literature. More large-scale data are needed to whether VBN can increase diagnostic yield (DY) of PPL.

Case report: Secondary failure to tolvaptan in a patient with SCLC and paraneoplastic SIADH

The syndrome of inappropriate antidiuretic hormone secretion (SIADH) is frequent in lung cancer patients. Here, we report a case with persistent hyponatremia, which suggested malignant SIADH and facilitated an early diagnosis of small cell lung cancer (SCLC). A combined radio-chemotherapy led to a partial remission and resolution of SIADH. An early relapse was indicated by reoccurring severe hyponatremia and increased copeptin levels, which were used as surrogate markers for the antidiuretic hormone (ADH). As palliative immunochemotherapy, together with fluid restriction and solute substitution, were unable to control hyponatremia, treatment with the ADH V2-receptor antagonist tolvaptan was initiated. Over time, the dose of tolvaptan needed to be increased, paralleled by a well-documented exponential increase of copeptin levels. In summary and conclusion, this is a rare case of a secondary failure to tolvaptan with unique documentary evidence of increasing copeptin levels. This observation supports the hypothesis that exceedingly high ADH levels may lead to competitive displacement of tolvaptan from the V2 receptor.

Regulatory mechanism and expression level of PRPS2 in lung cancer.

Lung cancer, with high morbidity and mortality, is the commonest respiratory system neoplasm, which seriously endangers the life safety of patients. In this study, the effect of PRPS2 on cell progression was preliminarily investigated. Immunohistochemical staining, western blot and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) were performed to verify the expression level of PRPS2 in lung cancer. Lung cancer cell lines with stable downregulation of PRPS2 were constructed in A549 cells and NCIH460 cells. The function of PRPS2 silencing on the proliferation ability was verified by the EdU and cell colony formation experiment. Scratch and transwell tests were conducted to verify the role of PRPS2 silencing on the migratory and invasive ability of cells. The impact of PRPS2 silencing on cell apoptosis and cell cycle was verified by flow cytometry test. The effects of PRPS2 silencing on apoptosis-associated proteins were assessed by western blot assay. The function of PRPS2 silencing on tumor growth in vivo was studied through xenograft tumor experiment. In comparison with normal tissues, PRPS2 was upregulated in lung cancer tissues. PRPS2 knockdown notably hindered the migratory ability, invasive ability and proliferation, but accelerated cell apoptosis. In vivo experiments confirmed that PRPS2 silencing blocked the growth of transplanted tumors. In lung cancer, PRPS2 silencing suppressed the malignant progression, indicating that PRPS2 might be a novel biomarker for lung cancer treatment and diagnosis.

Racial and Ethnic Disparities in Intensity of Care at the End of Life for Patients With Lung Cancer: A 13-Year Population-Based Study.

Lung cancer is the leading cause of cancer death in the United States. Disparities in lung cancer mortality among racial and ethnic minorities are well documented. Less is known as to whether racial and ethnic minority patients with lung cancer experience higher rates of intensity of care at the end of life (EOL) compared with non-Hispanic White (NHW) patients. We conducted a population-based analysis of patients 18 years and older with a lung cancer diagnosis who died between 2005 and 2018 using the California Cancer Registry linked to patient discharge data abstracts. Our primary outcome was intensity of care in the last 14 days before death (defined as any hospital admission or emergency department [ED] visit, intensive care unit [ICU] admission, intubation, cardiopulmonary resuscitation [CPR], hemodialysis, and death in an acute care setting). We used multivariable logistic regression models to evaluate associations between race and ethnicity and intensity of EOL care. Among 207,429 patients with lung cancer who died from 2005 to 2018, the median age was 74 years (range, 18-107) and 106,821 (51%) were male, 146,872 (70.8%) were NHW, 1,045 (0.5%) were American Indian, 21,697 (10.5%) were Asian Pacific Islander (API), 15,490 (7.5%) were Black, and 22,325 (10.8%) were Hispanic. Compared with NHW patients, in the last 14 days before death, API, Black, and Hispanic patients had greater odds of a hospital admission, an ICU admission, intubation, CPR, and hemodialysis and greater odds of a hospital or ED death. Compared with NHW patients, API, Black, and Hispanic patients who died with lung cancer experienced higher intensity of EOL care. Future studies should develop approaches to eliminate such racial and ethnic disparities in care delivery at the EOL.

Study on the difference and regularity of tongue images in 309 patients with different pathological stages of non-small cell lung cancer.

Tongue diagnosis is a crucial traditional Chinese medicine (TCM) inspection method for TCM syndrome differentiation and treatment. The primary research focus was on tongue image characteristic parameters of patients with non-small cell lung cancer (NSCLC). Analysis of the tongue image parameters of various pathological stages of NSCLC provides technical support for establishing an integrated Chinese and Western auxiliary diagnosis and efficacy evaluation medicine system for lung cancer that integrates tongue image features. Tongue image characteristics of 309 patients with NSCLC and 206 controls were collected and analyzed clinically. The T-test or rank sum test and logistic regression analysis were applied to analyze the characteristics of tongue image indicators of different pathological stages of NSCLC. There were differences in tongue image characteristics in the NSCLC group compared to the control group. The tongue quality and brightness of the tongue coating in the NSCLC group increased, the red component was reduced, the tongue coating thickened, and the yellow component increased compared to the healthy control group. A comparison of tongue image indexes of NSCLC in different pathological stages showed that stage IV had lower TB-b and higher TB-a than stage I. In addition, stage IV had lower TB-b than stage II + III, showing an increase in the blue and red components of the tongue in stage IV and the appearance of cyanotic tongue features. The tongue image characteristics of NSCLC patients differed from those of the control group. Tongue imaging indicators can reflect the characteristics of tongue images of patients with NSCLC. The tongue image characteristics of patients with stage IV lung cancer are bluish and purple compared with those with stage I, II, and III. It is suggested that the tongue's image characteristics can be used as a reference for the pathological classification of NSCLC and judgment of the disease process.

Lung cancer detection based on computed tomography image using convolutional neural networks.

Lung cancer is the most common type of cancer, accounting for 12.8% of cancer cases worldwide. As initially non-specific symptoms occur, it is difficult to diagnose in the early stages. Image processing techniques developed using machine learning methods have played a crucial role in the development of decision support systems. This study aimed to classify benign and malignant lung lesions with a deep learning approach and convolutional neural networks (CNNs). The image dataset includes 4459 Computed tomography (CT) scans (benign, 2242; malignant, 2217). The research type was retrospective; the case-control analysis. A method based on GoogLeNet architecture, which is one of the deep learning approaches, was used to make maximum inference on images and minimize manual control. The dataset used to develop the CNNs model is included in the training (3567) and testing (892) datasets. The model's highest accuracy rate in the training phase was estimated as 0.98. According to accuracy, sensitivity, specificity, positive predictive value, and negative predictive values of testing data, the highest classification performance ratio was positive predictive value with 0.984. The deep learning methods are beneficial in the diagnosis and classification of lung cancer through computed tomography images.

The value of dynamic FDG PET/CT in the differential diagnosis of lung cancer and predicting EGFR mutations

Objectives18F-fluorodeoxyglucose (FDG) PET/CT has been widely used for the differential diagnosis of cancer. Semi-quantitative standardized uptake value (SUV) is known to be affected by multiple factors and may make it difficult to differentiate between benign and malignant lesions. It is crucial to find reliable quantitative metabolic parameters to further support the diagnosis. This study aims to evaluate the value of the quantitative metabolic parameters derived from dynamic FDG PET/CT in the differential diagnosis of lung cancer and predicting epidermal growth factor receptor (EGFR) mutation status.MethodsWe included 147 patients with lung lesions to perform FDG PET/CT dynamic plus static imaging with informed consent. Based on the results of the postoperative pathology, the patients were divided into benign/malignant groups, adenocarcinoma (AC)/squamous carcinoma (SCC) groups, and EGFR-positive (EGFR+)/EGFR-negative (EGFR-) groups. Quantitative parameters including K1, k2, k3, and Ki of each lesion were obtained by applying the irreversible two-tissue compartmental modeling using an in-house Matlab software. The SUV analysis was performed based on conventional static scan data. Differences in each metabolic parameter among the group were analyzed. Wilcoxon rank-sum test, independent-samples T-test, and receiver-operating characteristic (ROC) analysis were performed to compare the diagnostic effects among the differentiated groups. P < 0.05 were considered statistically significant for all statistical tests.ResultsIn the malignant group (N = 124), the SUVmax, k2, k3, and Ki were higher than the benign group (N = 23), and all had-better performance in the differential diagnosis (P < 0.05, respectively). In the AC group (N = 88), the SUVmax, k3, and Ki were lower than in the SCC group, and such differences were statistically significant (P < 0.05, respectively). For ROC analysis, Ki with cut-off value of 0.0250 ml/g/min has better diagnostic specificity than SUVmax (AUC = 0.999 vs. 0.70). In AC group, 48 patients further underwent EGFR testing. In the EGFR (+) group (N = 31), the average Ki (0.0279 ± 0.0153 ml/g/min) was lower than EGFR (-) group (N = 17, 0.0405 ± 0.0199 ml/g/min), and the difference was significant (P < 0.05). However, SUVmax and k3 did not show such a difference between EGFR (+) and EGFR (-) groups (P>0.05, respectively). For ROC analysis, the Ki had a cut-off value of 0.0350 ml/g/min when predicting EGFR status, with a sensitivity of 0.710, a specificity of 0.588, and an AUC of 0.674 [0.523–0.802].ConclusionAlthough both techniques were specific, Ki had a greater specificity than SUVmax when the cut-off value was set at 0.0250 ml/g/min for the differential diagnosis of lung cancer. At a cut-off value of 0.0350 ml/g/min, there was a 0.710 sensitivity for EGFR status prediction. If EGFR testing is not available for a patient, dynamic imaging could be a valuable non-invasive screening method.

Cross-site validation of lung cancer diagnosis by electronic nose with deep learning: a multicenter prospective study

BackgroundAlthough electronic nose (eNose) has been intensively investigated for diagnosing lung cancer, cross-site validation remains a major obstacle to be overcome and no studies have yet been performed.MethodsPatients with lung cancer, as well as healthy control and diseased control groups, were prospectively recruited from two referral centers between 2019 and 2022. Deep learning models for detecting lung cancer with eNose breathprint were developed using training cohort from one site and then tested on cohort from the other site. Semi-Supervised Domain-Generalized (Semi-DG) Augmentation (SDA) and Noise-Shift Augmentation (NSA) methods with or without fine-tuning was applied to improve performance.ResultsIn this study, 231 participants were enrolled, comprising a training/validation cohort of 168 individuals (90 with lung cancer, 16 healthy controls, and 62 diseased controls) and a test cohort of 63 individuals (28 with lung cancer, 10 healthy controls, and 25 diseased controls). The model has satisfactory results in the validation cohort from the same hospital while directly applying the trained model to the test cohort yielded suboptimal results (AUC, 0.61, 95% CI: 0.47─0.76). The performance improved after applying data augmentation methods in the training cohort (SDA, AUC: 0.89 [0.81─0.97]; NSA, AUC:0.90 [0.89─1.00]). Additionally, after applying fine-tuning methods, the performance further improved (SDA plus fine-tuning, AUC:0.95 [0.89─1.00]; NSA plus fine-tuning, AUC:0.95 [0.90─1.00]).ConclusionOur study revealed that deep learning models developed for eNose breathprint can achieve cross-site validation with data augmentation and fine-tuning. Accordingly, eNose breathprints emerge as a convenient, non-invasive, and potentially generalizable solution for lung cancer detection.Clinical trial registrationThis study is not a clinical trial and was therefore not registered.

Surveillance of subsolid nodules avoids unnecessary resections in lung cancer screening: Long-term results of the prospective BioMILD trial

BackgroundThe management of sub-solid nodules (SSNs) in lung cancer screening (LCS) is still a topic of debate, with no current uniform strategy to deal with these lesions at risk of overdiagnosis and overtreatment.ObjectiveThe BioMILD LCS trial has implemented a prospective conservative approach for SSNs, managing with annual low-dose CT nonsolid nodules (NSNs) and part-solid nodules (PSNs) with a solid component &lt;5 mm, regardless of the size of the nonsolid component. The present study aims to determine the lung cancer (LC) detection and survival in BioMILD volunteers with SSNs.Materials and methodsEligible participants were 758 out of 4071 (18.6%) BioMILD volunteers without baseline LC and at least one SSN detected at the baseline or further LDCT rounds. The outcomes of the study were LC detection and long-term survival.ResultsA total of 844 NSNs and 241 PSNs were included. LC detection was 3.7% (31/844) in NSNs and 7.1% (17/241) in PSNs, being significantly greater in prevalent than incident nodules (8.4%versus1.3% in NSNs; 14.1%versus2.1% in PSNs; p-value for both nodule types&lt;0.01). Most LCs from SSNs were stage I (42/48, 87.5%), resectable (47/48, 97.9%), and caused no deaths. The 8-year cumulative survival of volunteers with LC derived from SSNs and not derived from SSNs was 93.8% and 74.9%, respectively.ConclusionConservative management of SSNs in LCS enables timely diagnosis and treatment of LCs arising from SSNs while ensuring the resection of more aggressive LCs detected away from SSNs.

Microbes for lung cancer detection: feasibility and limitations

As the second most common cancer in the world, the development of lung cancer is closely related to factors such as heredity, environmental exposure, and lung microenvironment, etc. Early screening and diagnosis of lung cancer can be helpful for the treatment of patients. Currently, CT screening and histopathologic biopsy are widely used in the clinical detection of lung cancer, but they have many disadvantages such as false positives and invasive operations. Microbes are another genome of the human body, which has recently been shown to be closely related to chronic inflammatory, metabolic processes in the host. At the same time, they are important players in cancer development, progression, treatment, and prognosis. The use of microbes for cancer therapy has been extensively studied, however, the diagnostic role of microbes is still unclear. This review aims to summarize recent research on using microbes for lung cancer detection and present the current shortcomings of microbes in collection and detection. Finally, it also looks ahead to the clinical benefits that may accrue to patients in the future about screening and early detection.

An Overview of Lung Cancer

Lung cancer is characterised by unregulated cell growth. The most prevalent cancer killer worldwide is lung cancer. Lung cancer diagnoses and deaths are rising worldwide. Males and females over 70 have the highest lung cancer risk. Since 50% of lung cancer patients acquire a new cough, smokers or former smokers should be concerned. Lung cancer's complex pathogenesis is yet unknown. Smoking and carcinogen exposure can cause lung epithelial dysplasia. The most prevalent lung cancer diagnosis methods are flexible bronchoscopy and transthoracic sampling. Immunotherapy helps the immune system recognise and fight cancer cells as foreign intruders. Radiation and four to six chemotherapy cycles are usual for mediastinal or hilar lymph node LS-SCLC.

A multichannel analysis of imbalanced computed tomography data for lung cancer classification

Lung cancer holds the highest fatality rate among cancers, emphasizing the importance of early detection. Computer algorithms have gained prominence across various domains, including lung cancer diagnosis. These algorithms assist specialists, especially in medical imaging, yet current efforts lack comprehensive CT data analysis; especially in handling imbalanced datasets and fully exploiting spatial information. The lack of spatial analysis hinders the ability to identify subtle variations in texture and structure that are crucial for detecting lung cancer early and accurately. Therefore, this study uses a multichannel analysis of computed tomography (CT) images and deep learning-based ensemble learning (MC-ECNN) to find lung cancer even when the data is not balanced. Firstly, the data imbalance issue is tackled through the synthetic minority oversampling technique (SOMTE); afterwards, a multi-channel analysis of the data is performed to explore a distinct set of abstract features. Lastly, a deep ensemble learning method is used to classify the extracted distinct abstract feature set into the appropriate classes. The proposed method uses the discrete Fast Fourier transform (DFFT) and discrete cosine transform (DCT), along with the actual CT scans, for the multi-channel analysis of the data in different domains. The proposed model yielded 99.60% test accuracy on unseen data, which is at least 3% better than the other state-of-the-art studies considered for the comparison. In addition to the classification accuracy, the efficacy of the proposed model has also been justified through precision, recall, F1-score, support value, and misclassification rate.

Bronchoalveolar Lavage Fluid Microbiota is Associated with the Diagnosis and Prognosis Evaluation of Lung Cancer

Bronchoalveolar Lavage Fluid Microbiota is Associated with the Diagnosis and Prognosis Evaluation of Lung Cancer

Multiside graph neural network-based attention for local co-occurrence features fusion in lung nodule classification

Early diagnosis of lung cancer is critical as it can save people’s lives. Long-range dependencies within volumetric medical images are essential attributes for accurate lung nodule classification. Many deep learning-based methods are used for lung nodule classification; however, the construction of the lung nodule is not axial and can be any shape. Thus, the nodules are interrelated through their adjacent slices axially and diagonally, locally and globally, making their capture through local convolutional operations challenging. In this article, we benefited from local co-occurrences of texture features and graph neural networks (GNNs) to effectively capture important patterns considering the long-range dependencies among adjusted slices. The proposed framework comprises a multi-side graph construction layer (MSGCL) that computes informative texture features and captures spatial relationships from cross-sectional and longitude orientations of the nodule, creating two sets of nodes. Further, the graph-based fusion of long-range dependency layers (GFLL) is used to deeply fuse and generate attentive edges among fused nodes. The LIDC-IDRI dataset, used for training and testing the proposed Multi-side Graph Neural Network-based Attention for Local Co-occurrence Features Fusion (MS-GNN-ALCFF), achieves state-of-the-art. The LUNGx dataset used as an unseen dataset shows that our model is generalisable compared to baselines. The results of the proposed method when the LIDC-IDRI dataset was used for train and testing were 87.17 ± 0.84 %, 91.01 ± 1.14 %, 88.6 ± 1.42 %, 89.7 ± 0.87 %,95 ± 1.22 % and 76.7 ± 1.4 % in terms of Accuracy, Precision, Recall, F1-score, AUC and MCC respectively. When the LUNGx dataset is used for testing as an unseen dataset, the results were 69.86 ± 2.4 %, 75 ± 2.7 %, 71.4 ± 3.1 %, 73.17 ± 3.7 %, 70.2 ± 1.36 % and 71 ± 3.8 % for Accuracy, Precision, Recall, F1-score, AUC and MCC respectively. These findings represent the significance of the ability of GNNs to construct a multi-set of nodes through the proposed MSGCL layer and fuse deeply through the proposed GFLL layer.

Five years of advances in electrochemical analysis of protein biomarkers in lung cancer: a systematic review.

Lung cancer is the leading cause of cancer death in both men and women. It represents a public health problem that must be addressed through the early detection of specific biomarkers and effective treatment. To address this critical issue, it is imperative to implement effective methodologies for specific biomarker detection of lung cancer in real clinical samples. Electrochemical methods, including microfluidic devices and biosensors, can obtain robust results that reduce time, cost, and assay complexity. This comprehensive review will explore specific studies, methodologies, and detection limits and contribute to the depth of the discussion, making it a valuable resource for researchers and clinicians interested in lung cancer diagnosis.

LRFE: A NOVEL LOCAL RESPONSE FEATURE ELIMINATION PROCESS FOR IDENTIFICATION OF LUNG CANCER CELLS

One of the main causes of cancer-related mortality across the globe is lung cancer. Early-stage lung cancer frequently exhibits no symptoms, which delays diagnosis until the illness has progressed. Before symptoms manifest, screening and early detection techniques can aid in the early diagnosis and treatment of lung cancer. Many olden research papers have implemented image processing and few latest papers have implemented computer vision techniques to detect lung cancer. Particularly when dealing with minor or subtle anomalies, image processing algorithms may not be able to detect lung cancer lesions with sufficient sensitivity and specificity. It is still difficult to increase the detection algorithms' accuracy and dependability, especially when dealing with early-stage lesions or situations where attributes overlap. It takes a lot of processing power, such as high-performance GPUs and enormous memory capacities, to train deep learning models, particularly large-scale convolutional neural networks (CNNs). In this proposed research, the model pre-processes the images using the ostu and sober filter mechanisms because Otsu's approach adjusts to the features of the input image, including noise, contrast, and lighting fluctuations. It is capable of handling images with varying dynamic ranges and intensity distributions without depending on pre-established threshold settings. When it comes to image noise, the Sobel filter is more resilient than other edge detection methods. It produces clearer edge maps and fewer false detections by determining the gradient magnitude, which amplifies edge information while suppressing noise. The features are extracted using the tuned AlexNet pre-trained model, in AlexNet there is a layer known as “Layer-wise Relevance Propagation”. By giving each pixel or feature in the input image a relevance score, the LRP layer offers fine-grained feature attribution. This makes it possible to analyze in great depth which particular elements or areas of the input image are most important for the network to forecast, which helps to clarify the underlying patterns that the network has learned. At last, the extracted features are further reduced using the enhanced feature elimination method. By iteratively selecting subsets of features based on their importance, RFE helps to identify the most relevant features for the classification task. Integrating RFE with SVM classifiers can lead to improved model performance by focusing on the subset of features that are most discriminative and informative for the classification problem.

Diagnosis of Medical Images Using Convolutional Neural Networks

Medical image diagnosis using Convolutional Neural Networks (CNNs) has emerged as a viable way to improve the accuracy and efficiency of disease identification and categorization in clinical settings. In this study, they look at how CNNs can be used to diagnose lung nodules from chest X-ray pictures, to provide insights into the technology's performance and future clinical applications. A dataset of 10,000 tagged chest X-ray pictures showing both benign and malignant lung nodules was obtained and preprocessed using standard methods. The dataset was used to construct and train a proprietary CNN architecture, which was then rigorously evaluated on distinct training, validation, and test sets. The CNN model showed good accuracy (94.8%), sensitivity (92.1%), specificity (96.5%), precision, recall, F1 score, and area under the ROC curve (AUC), indicating its robustness and generalization ability. These findings show that CNN-based diagnostic tools may help radiologists and physicians discover and diagnose lung cancer earlier, improving patient outcomes and optimizing healthcare delivery. However, difficulties such as interpretability, data privacy, and regulatory approval must be addressed before CNNs can be fully utilized in medical imaging. This study emphasizes CNNs' transformative significance in diagnostic medicine and the necessity for additional research and development to realize their full potential in clinical practice.