Classification Of Nodules Research Articles

Role of Macrocalcification in Raising the Suspicion of Malignancy among the ACR TIRADS Thyroid Nodules Detected by Ultrasonography

Abstract Background Thyroid nodules are a frequent finding on neck sonography. The US is the most commonly used imaging technique for the evaluation of thyroid nodules. The Thyroid Imaging, Reporting and Data System (TIRADS) developed by the American college of radiology (ACR) for classification of thyroid nodules is a practical and convenient method for risk stratification of thyroid malignancy, based on allocating points for different sonographic features of thyroid nodules in five morphologic categories (composition, echogenicity, margins, shape, and presence of echogenic foci). Objective To evaluate the role of macrocalcification in raising the suspicious of malignancy among ACR TIRADS 3 and 4 group without macrocalcification Patients and Methods Our study was conducted on 60 adult patients, 20 patients with TIRADS 3 nodule, 20 patients with TIRADS 4 group with macrocalcification nodules and 20 patients group TIRADS 4 without macrocalcification nodules, whose ages range between 23 and 65 years old and were referred to undergo FNA procedures. Results The present study concludes parenchymal macrocalcifications showed no statistically significant difference in raising percentage of malignancy among TIRADS 4 patients. Conclusion We conclude that the added macrocalcification showed no statistically significant difference in raising percentage of malignancy compared to TIRADS 4 without calcification group. Also, presence of macrocalcification should not prevent clinicians from making further assessments in case of nondiagnostic results to exclude their association with rather than their causal relation with thyroid malignancies.

Research on Performance Metrics and Augmentation Methods in Lung Nodule Classification

Lung nodule classification is crucial for the diagnosis and treatment of lung diseases. However, selecting appropriate metrics to evaluate classifier performance is challenging, due to the prevalence of negative samples over positive ones, resulting in imbalanced datasets. This imbalance often necessitates the augmentation of positive samples to train powerful models effectively. Furthermore, specific medical tasks require tailored augmentation methods, the effectiveness of which merits further exploration based on task objectives. This study conducted a detailed analysis of commonly used metrics in lung nodule detection, examining their characteristics and selecting suitable metrics based on this analysis and our experimental findings. The selected metrics were then applied to assessing different combinations of image augmentation techniques for nodule classification. Ultimately, the most effective metric was identified, leading to the determination of the most advantageous augmentation method combinations.

Exploring the Effect of Domain-Specific Transfer Learning for Thyroid Nodule Classification

Exploring the Effect of Domain-Specific Transfer Learning for Thyroid Nodule Classification

Clinical risk factors and cancer risk of thyroid imaging reporting and data system category 4 A thyroid nodules

PurposeBeyond the Thyroid Imaging Reporting and Data System (TIRADS) classification of thyroid nodules, additional factors must be weighed in the decision to perform fine needle aspiration (FNA). In this study, we aimed to identify risk factors for malignancy in patients with ultrasound-classified Chinese-TIRADS (C-TIRADS) 4 A nodules.MethodsPatients who underwent thyroid FNA at our institution between May 2021 and September 2022 were enrolled. We collected demographic data, including age, sex, previous radiation exposure, and family history. An in-person questionnaire was used to collect lifestyle data, such as smoking habits and alcohol consumption. Body mass index (BMI) was calculated. The serum levels of thyroid stimulating hormone (TSH), thyroid peroxidase antibody (TPOAb), and thyroglobulin antibody (TGAb) were measured. Prior to FNA, ultrasonic inspection reports were reviewed. The cytologic diagnoses for FNA of thyroid nodules followed the Bethesda System for Reporting Thyroid Cytopathology (2017).ResultsAmong the 252 C-TIRADS 4 A nodules, 103 were malignant. Compared to those in the benign group, the patients in the malignant group had a younger age (42.2 ± 13.6 vs. 51.5 ± 14.0 years, P < 0.001). Logistic regression showed that advanced age was associated with a lower risk of malignancy in C-TIRADS 4 A nodules (OR = 0.95, 95% CI 0.93 ~ 0.97, P < 0.001). We demonstrated a decreased risk of malignancy in patients with 48.5 years or older.ConclusionAdvanced age was associated with a decreased risk of malignancy in patients with C-TIRADS 4 A nodules. This study indicated that in addition to sonographic characteristics, patient age should be considered when assessing the risk of malignancy.

Performance analysis of various deep learning models based on Max-Min CNN for lung nodule classification on CT images

Performance analysis of various deep learning models based on Max-Min CNN for lung nodule classification on CT images

RAS-Mutant Follicular Thyroid Tumors: A Continuous Challenge for Pathologists.

The classification of thyroid nodules, particularly those with a follicular growth pattern, has significantly evolved. These tumors, enriched with RAS or RAS-like mutations, remain challenging for pathologists due to variables such as nuclear atypia, invasion, mitotic activity, and tumor necrosis. This review addresses the histological correlates of benign, low-risk, and malignant RAS-mutant thyroid tumors, as well as some difficult-to-classify follicular nodules with worrisome features. One prototypical RAS-mutant nodule is non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP). The assessment of nuclear characteristics in encapsulated/well-demarcated non-invasive RAS-mutant follicular-patterned tumors helps distinguish between follicular thyroid adenoma (FTA) and NIFTP. Despite this straightforward concept, questions about the degree of nuclear atypia necessary for the diagnosis of NIFTP are common in clinical practice. The nomenclature of follicular nodules lacking clear invasive features with increased mitotic activity, tumor necrosis, and/or high-risk mutations (e.g., TERT promoter or TP53) remains debated. Invasion, particularly angioinvasion, is the current hallmark of malignancy in RAS-mutant follicular-patterned neoplasms, with follicular thyroid carcinoma (FTC) as the model. Assessing the tumor interface is critical, though full capsule evaluation can be challenging. Multiple levels and NRASQ61R-specific immunohistochemistry can aid in identifying invasion. Controversies around vascular invasion persist, with ancillary stains like CD31, ERG, and CD61 aiding in its evaluation. Moreover, the review highlights that invasive encapsulated follicular variant papillary thyroid carcinoma (IEFVPTC) is closely associated with FTC, suggesting the need for better nomenclature. The concept of "high-grade" differentiated carcinomas, applicable to FTC or IEFVPTC with necrosis and/or high mitotic activity, is also discussed.

VSNet: classification of pulmonary nodules in 3D using vision transformer and sequence spatial attention mechanism

VSNet: classification of pulmonary nodules in 3D using vision transformer and sequence spatial attention mechanism

A liquid biopsy-based assay for classifying breast cancer from benign nodule.

e12572 Background: Breast cancer (BRCA) ranks as the most frequently diagnosed non-skin cancer and stands as the second leading cause of cancer-related mortality in women in the United States. Globally, 2.3 million women were diagnosed with breast cancer in 2020, resulting in 685,000 deaths. Therefore, accurate classification of breast cancer could contribute to immediate treatment and improve outcomes for individuals diagnosed with breast cancer. Our study introduces an ensemble model utilizing the distinctive characteristics of cell-free DNA (cfDNA) fragmentation from whole genome sequencing (WGS) for the classification of BRCA and benign nodule. Methods: The study cohort consisted of 214 individuals diagnosed with breast cancer (BRCA) and 211 individuals with benign breast nodule. Employing whole-genome sequencing (WGS), we subjected plasma samples from all participants to comprehensive fragmentomic profiling. Three fragmentomic features: copy number variation (CNV), fragmentation size coverage (FSC), and fragmentation size distribution (FSD), were utilized by machine learning algorithms. An ensemble model integrated results from each algorithm then generated a cancer score ranging from 0 to 1 for each sample. The model was trained exclusively using the training cohort and the model’s performance was assessed in the independent validation cohort. Results: Our ensemble model demonstrated a sensitivity of 80.2% and a specificity of 95.3% in training cohort, with an AUC of 0.966. In the validation cohort, the model maintained its effectiveness, with a sensitivity of 73.6%, a specificity of 89.3% supported by an AUC of 0.919. Notably, the model showed various predictive ability across different stages of breast cancer. In the training cohort, it exhibited a sensitivity of 76.4% for Stage I, 81.8% for Stage II, and 88.9% for Stage III, indicating superior predictive power at advanced progression levels. Additionally, the model demonstrated a consistent sensitivity of 75% for Stage I in the validation cohort, affirming its reliable performance across independent datasets. When stratified by molecular subtypes, the model showed relatively lower scores and sensitivity BRCA with HR+/HER2+, compared to HR-/HER2+ and HR+/HER2- subgroups, as well as Triple-Negative Breast Cancer. Finally, the model generated unbiased predictions across various histology subtypes, accurately discerning ductal carcinoma and other BRCA histology subtypes in training and validation cohorts. Conclusions: The ensemble model that leverages non-invasive liquid biopsy-based assay for the accurate classification of BRCA and benign nodule provides a critical opportunity for effective intervention for cancer patients. This approach, facilitating swift and appropriate treatments, and optimizing the allocation of healthcare resources, underscores its significance in the ongoing pursuit of enhancing breast cancer care and outcomes.

Correlation between ultrasonographic and cytologic features of thyroid nodules: a single-center cross-sectional study.

A thyroid nodule is managed according to the clinical context, ultrasound (US) findings, and fine needle aspiration (FNA) results. Most thyroid nodules are benign; however, nodule classification is crucial to avoid unnecessary thyroid surgery. We conducted this study to compare the findings of fine-needle aspiration cytology (FNAC) expressed using the Bethesda system with the features of thyroid US classified using the EU-TIRADS classification to assess the risk of malignancy. A descriptive and analytical study involving 99 patients with thyroid nodules followed up in the Department of Endocrinology-Diabetology and Nutrition. Data were collected from medical records and analyzed using SPSS software V21. FNA was performed on 121 nodules using the BETHESDA system. These nodules were classified as malignant, suspicious for follicular neoplasm, and suspicious for malignancy in 5.8%, 5%, and 1.7% of cases, respectively. As for the EU-TIRADS 2017 classification, 59.5% of benign nodules were classified as EU-TIRADS III, whereas 66.7% of malignant nodules were classified as EU-TIRADS V and significantly related to malignant prediction (P = 0.000). The size of nodules was significantly correlated to the risk of malignancy (P = 0.013). Seventy-five percent of nodules with central vascularity were malignant (P = 0.012). Irregularity of nodule contours was significantly associated with the risk of malignancy, as 30% of nodules with irregular contours were Bethesda VI (P = 0.003). Hypoechogenicity was found in 77.8% of malignant nodules (P = 0.004). Additionally, only 9.2% of the nodules were taller than wide, of which 37.5% were malignant (P = 0.012). For a safe management strategy, US-guided FNAC should be performed on each suspicious thyroid nodule, given the correlation between EU-TIRADS classification features and the risk of malignancy.

Automated 3D convolutional neural network architecture design using genetic algorithm for pulmonary nodule classification

Cancer of the lungs is considered one of the primary causes of death among patients globally. Early detection contributes significantly to the success of pulmonary cancer treatment. To aid the pulmonary nodule classification, many models for the analysis of medical image utilizing deep learning have been developed. Convolutional neural network (CNN) recently, has attained remarkable results in various image classification tasks. Nevertheless, the CNNs performance is heavily dependent on their architectures which still heavily reliant on human domain knowledge. This study introduces a cutting-edge approach that leverages genetic algorithms (GAs) to automatically design 3D CNN architectures for differentiation between benign and malignant pulmonary nodules. The suggested algorithm utilizes the dataset of lung nodule analysis 2016 (LUNA16) for evaluation. Notably, our approach achieved exceptional model accuracy, with evaluations on the testing dataset yielding up to 95.977%. Furthermore, the algorithm exhibited high sensitivity, showcasing its robust performance in distinguishing between benign and malignant nodules. Our findings demonstrate the outstanding capabilities of the proposed algorithm and show an outstanding performance and attain a state of art solution in lung nodule classification.

Leveraging Serial Low-Dose CT Scans in Radiomics-based Reinforcement Learning to Improve Early Diagnosis of Lung Cancer at Baseline Screening.

Purpose To evaluate the feasibility of leveraging serial low-dose CT (LDCT) scans to develop a radiomics-based reinforcement learning (RRL) model for improving early diagnosis of lung cancer at baseline screening. Materials and Methods In this retrospective study, 1951 participants (female patients, 822; median age, 61 years [range, 55-74 years]) (male patients, 1129; median age, 62 years [range, 55-74 years]) were randomly selected from the National Lung Screening Trial between August 2002 and April 2004. An RRL model using serial LDCT scans (S-RRL) was trained and validated using data from 1404 participants (372 with lung cancer) containing 2525 available serial LDCT scans up to 3 years. A baseline RRL (B-RRL) model was trained with only LDCT scans acquired at baseline screening for comparison. The 547 held-out individuals (150 with lung cancer) were used as an independent test set for performance evaluation. The area under the receiver operating characteristic curve (AUC) and the net reclassification index (NRI) were used to assess the performances of the models in the classification of screen-detected nodules. Results Deployment to the held-out baseline scans showed that the S-RRL model achieved a significantly higher test AUC (0.88 [95% CI: 0.85, 0.91]) than both the Brock model (AUC, 0.84 [95% CI: 0.81, 0.88]; P = .02) and the B-RRL model (AUC, 0.86 [95% CI: 0.83, 0.90]; P = .02). Lung cancer risk stratification was significantly improved by the S-RRL model as compared with Lung CT Screening Reporting and Data System (NRI, 0.29; P < .001) and the Brock model (NRI, 0.12; P = .008). Conclusion The S-RRL model demonstrated the potential to improve early diagnosis and risk stratification for lung cancer at baseline screening as compared with the B-RRL model and clinical models. Keywords: Radiomics-based Reinforcement Learning, Lung Cancer Screening, Low-Dose CT, Machine Learning © RSNA, 2024 Supplemental material is available for this article.

Classification of benign and malignant pulmonary nodule based on local-global hybrid network.

The accurate classification of pulmonary nodules has great application value in assisting doctors in diagnosing conditions and meeting clinical needs. However, the complexity and heterogeneity of pulmonary nodules make it difficult to extract valuable characteristics of pulmonary nodules, so it is still challenging to achieve high-accuracy classification of pulmonary nodules. In this paper, we propose a local-global hybrid network (LGHNet) to jointly model local and global information to improve the classification ability of benign and malignant pulmonary nodules. First, we introduce the multi-scale local (MSL) block, which splits the input tensor into multiple channel groups, utilizing dilated convolutions with different dilation rates and efficient channel attention to extract fine-grained local information at different scales. Secondly, we design the hybrid attention (HA) block to capture long-range dependencies in spatial and channel dimensions to enhance the representation of global features. Experiments are carried out on the publicly available LIDC-IDRI and LUNGx datasets, and the accuracy, sensitivity, precision, specificity, and area under the curve (AUC) of the LIDC-IDRI dataset are 94.42%, 94.25%, 93.05%, 92.87%, and 97.26%, respectively. The AUC on the LUNGx dataset was 79.26%. The above classification results are superior to the state-of-the-art methods, indicating that the network has better classification performance and generalization ability.

Application of Artificial Intelligence in Neuroendocrine Lung Cancer Diagnosis and Treatment: A Systematic Review.

Neuroendocrine tumors (NETs) represent a heterogeneous group of neoplasms with diverse clinical presentations and prognoses. Accurate and timely diagnosis of these tumors is crucial for appropriate management and improved patient outcomes.In recent years, exciting advancements in artificial intelligence (AI) technologies have been revolutionizing medical diagnostics, particularly in the realm of detecting and characterizing pulmonary NETs, offering promisingavenuesfor improved patient care. This article aims to provide a comprehensive overview of the role of AI in diagnosing lung NETs. We discuss the current challenges associated with conventional diagnostic approaches, including histopathological examination and imaging modalities.Despite advancements in these techniques, accurate diagnosis remains challenging due to the overlapping features with other pulmonary lesions and the subjective interpretation of imaging findings. AI-based approaches, including machine learning and deep learning algorithms, have demonstrated remarkable potential in addressing these challenges. By leveraging large datasets of radiological images, histopathological samples, and clinical data, AI models can extract complex patterns and features that may not be readily discernible to human observers. Moreover, AI algorithms can continuously learn and improve from new data, leading to enhanced diagnostic accuracy and efficiency over time. Specific AI applications in the diagnosis of lung NETs include computer-aided detection and classification of pulmonary nodules on CT scans, quantitative analysis of PET imaging for tumor characterization, and integration of multi-modal data for comprehensive diagnostic assessments. These AI-driven tools hold promise for facilitating early detection, risk stratification, and personalized treatment planning in patients with lung NETs.

LDCT image biomarkers that matter most for the deep learning classification of indeterminate pulmonary nodules.

Continued improvement in deep learning methodologies has increased the rate at which deep neural networks are being evaluated for medical applications, including diagnosis of lung cancer. However, there has been limited exploration of the underlying radiological characteristics that the network relies on to identify lung cancer in computed tomography (CT) images. In this study, we used a combination of image masking and saliency activation maps to systematically explore the contributions of both parenchymal and tumor regions in a CT image to the classification of indeterminate lung nodules. We selected individuals from the National Lung Screening Trial (NLST) with solid pulmonary nodules 4-20 mm in diameter. Segmentation masks were used to generate three distinct datasets; 1) an Original Dataset containing the complete low-dose CT scans from the NLST, 2) a Parenchyma-Only Dataset in which the tumor regions were covered by a mask, and 3) a Tumor-Only Dataset in which only the tumor regions were included. The Original Dataset significantly outperformed the Parenchyma-Only Dataset and the Tumor-Only Dataset with an AUC of 80.80 ± 3.77% compared to 76.39 ± 3.16% and 78.11 ± 4.32%, respectively. Gradient-weighted class activation mapping (Grad-CAM) of the Original Dataset showed increased attention was being given to the nodule and the tumor-parenchyma boundary when nodules were classified as malignant. This pattern of attention remained unchanged in the case of the Parenchyma-Only Dataset. Nodule size and first-order statistical features of the nodules were significantly different with the average malignant and benign nodule maximum 3d diameter being 23mm and 12mm, respectively. We conclude that network performance is linked to textural features of nodules such as kurtosis, entropy and intensity, as well as morphological features such as sphericity and diameter. Furthermore, textural features are more positively associated with malignancy than morphological features.

Classification of Benign-Malignant Thyroid Nodules Based on Hyperspectral Technology.

In recent years, the incidence of thyroid cancer has rapidly increased. To address the issue of the inefficient diagnosis of thyroid cancer during surgery, we propose a rapid method for the diagnosis of benign and malignant thyroid nodules based on hyperspectral technology. Firstly, using our self-developed thyroid nodule hyperspectral acquisition system, data for a large number of diverse thyroid nodule samples were obtained, providing a foundation for subsequent diagnosis. Secondly, to better meet clinical practical needs, we address the current situation of medical hyperspectral image classification research being mainly focused on pixel-based region segmentation, by proposing a method for nodule classification as benign or malignant based on thyroid nodule hyperspectral data blocks. Using 3D CNN and VGG16 networks as a basis, we designed a neural network algorithm (V3Dnet) for classification based on three-dimensional hyperspectral data blocks. In the case of a dataset with a block size of 50 × 50 × 196, the classification accuracy for benign and malignant samples reaches 84.63%. We also investigated the impact of data block size on the classification performance and constructed a classification model that includes thyroid nodule sample acquisition, hyperspectral data preprocessing, and an algorithm for thyroid nodule classification as benign and malignant based on hyperspectral data blocks. The proposed model for thyroid nodule classification is expected to be applied in thyroid surgery, thereby improving surgical accuracy and providing strong support for scientific research in related fields.

A comparison study of artificial intelligence performance against physicians in benign–malignant classification of pulmonary nodules

Abstract Objectives To compare and evaluate the performance of artificial intelligence (AI) against physicians in classifying benign and malignant pulmonary nodules from computerized tomography (CT) images. Methods A total of 506 CT images with pulmonary nodules were retrospectively collected. The AI was trained using in-house software. For comparing the diagnostic performance of artificial intelligence and different groups of physicians in pulmonary nodules, statistical methods of receiver operating characteristic (ROC) curve and area under the curve (AUC) were analyzed. The nodules in CT images were analyzed in a case-by-case manner. Results The diagnostic accuracy of AI surpassed that of all groups of physicians, exhibiting an AUC of 0.88 alongside a sensitivity of 0.80, specificity of 0.84, and accuracy of 0.83. The area under the curve (AUC) of seven groups of physicians varies between 0.63 and 0.84. The sensitivity of the physicians within these groups varies between 0.4 and 0.76. The specificity of different groups ranges from 0.8 to 0.85. Furthermore, the accuracy of the seven groups ranges from 0.7 to 0.82. The professional insights for enhancing deep learning models were obtained through an examination conducted on a per-case basis. Conclusions AI demonstrated great potential in the benign–malignant classification of pulmonary nodules with higher accuracy. More accurate information will be provided by AI when making clinical decisions.

An enhanced Convolutional Neural Network (CNN) model for the detection of lung cancer using X-Ray image

Lung Cancer is a life-threatening disease which can be diagnosed by Medical Imaging such as X-Ray, MRI, CT Scan etc. This research presented an enhanced model using Convolutional Neural Network (CNN) to detect lung cancer using X-Ray image. Medical image processing relies heavily on the diagnosis of lung cancer images. It aids doctors in determining the correct diagnosis and management. For many patients, lung cancer ranks among the mostdeadly diseases. Many lives can be saved if cancerous growth is diagnosed early. The purported model was predominantly built on Convolutional Neural Network (CNN) architecture and the model was built with enhanced features such as Image Enhancement, Segmenting ROI (Region of Interest), Features Extraction and Nodule Classification. In preprocessing stage, the AMF (Adaptive Median Filter) filtering method was applied to eliminate noise in X-Ray image of the dataset, and quality of X-Ray image was improved with the support of CLAHE (Contrast Limited Adaptive Histogram Equalization). Secondly, K-means Clustering algorithm was used to extract the relevant feature or Region of Interest (ROI) of the lung field automatically i.e. the model was effectively trained to identify and crop the exact location of the lung field automatically. The model was able to classify the cancer nodule as either Cancerous or Non-Cancerous. The framework worked on C# platform, and used EMGU for detection of the tumour in the lung xray image. Experimental result showed that the developed system was able to detect Lung Cancer with 90.77% accuracy, 86.65% precision and 95.31% Recall/Sensitivity.

Hybrid deep learning assisted multi classification: Grading of malignant thyroid nodules.

Thyroid nodules are commonly diagnosed with ultrasonography, which includes internal characteristics, varying looks, and hazy boundaries, making it challenging for a clinician to differentiate between malignant and benign forms based only on visual identification. The advancement of AI, particularly DL, provides significant breakthroughs in the domain of medical image identification. Yet, there are certain obstacles to achieving accuracy as well as efficacy in thyroid nodule detection. The thyroid nodules in this study are detected and classified using an inventive hybrid deep learning-assisted multi-classification method. The median blur method is applied in this work to eliminate the salt and pepper noise from the image. Then MPIU-Net-based segmentation is utilized to segment the image. The LGBPNP-based features are retrieved from the segmented image to obtain a single histogram sequence of the LGBP pattern in addition to other features like extraction of multi-texton and LTP-based features. After the feature extraction, the data augmentation process is applied and then the features are fed to the hybrid classification-based nodule classification model that comprises Deep Maxout and CNN, this hybrid classification trains the features and predicts the thyroid nodule. Additionally, the TIRADS score classification is used for the projected malignant thyroid nodule coupled with statistical features collected from the segmented. The DBNAAF with transfer learning model is employed to classify the grading of malignant thyroid nodules, where the weights of the model are learned with transfer learning. The MCC of the Hybrid Model is 0.9445, whereas the DCNN is 0.6858, YOLOV3-DMRF is 0.7229, CNN is 0.7780, DBN is 0.7601, Bi-GRU is 0.7038, Deep Maxout is 0.7528, and RNN is 0.8522, respectively.

Quantum-enhanced hybrid feature engineering in thoracic CT image analysis for state-of-the-art nodule classification: an advanced lung cancer assessment

The intricate nature of lung cancer treatment poses considerable challenges upon diagnosis. Early detection plays a pivotal role in mitigating its escalating global mortality rates. Consequently, there are pressing demands for robust and dependable early detection and diagnostic systems. However, the technological limitations and complexity of the disease make it challenging to implement an efficient lung cancer screening system. AI-based CT image analysis techniques are showing significant contributions to the development of computer-assisted detection (CAD) systems for lung cancer screening. Various existing research groups are working on implementing CT image analysis systems for assessing and classifying lung cancer. However, the complexity of different structures inside the CT image is high and comprehension of significant information inherited by them is more complex even after applying advanced feature extraction and feature selection techniques. Traditional and classical feature selection techniques may struggle to capture complex interdependencies between features. They may get stuck in local optima and sometimes require additional exploration strategies. Traditional techniques may also struggle with combinatorial optimization problems when applied to a prominent feature space. This paper proposed a methodology to overcome the existing challenges by applying feature extraction using Vision Transformer (FexViT) and Feature selection using the Quantum Computing based Quadratic unconstrained binary optimization (QC-FSelQUBO) technique. This algorithm shows better performance when compared with other existing techniques. The proposed methodology showed better performance as compared to other existing techniques when evaluated by applying necessary output measures, such as accuracy, Area under roc (receiver operating characteristics) curve, precision, sensitivity, and specificity, obtained as 94.28%, 99.10%, 96.17%, 90.16% and 97.46%. The further advancement of CAD systems is essential to meet the demand for more reliable detection and diagnosis of cancer, which can be addressed by leading the proposed quantum computation and growing AI-based technology ahead.

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.