Detection Of Nodules Research Articles

Integrating Yolo V5 Analysis and KNN to Improve Lung Cancer Detection

Lung cancer is a leading cause of cancer-related mortality globally, emphasizing the urgent need for early detection and accurate diagnosis. This project aims to leverage advanced deep learning techniques, specifically YOLO-v5 (You Only Look Once) for object detection, and the k-Nearest Neighbors (kNN) algorithm for unsupervised learning, to enhance the detection and analysis of lung cancer from CT scan images. YOLO-v5, known for its exceptional speed and accuracy in detecting objects within images, will be used to identify and localize lung nodules, which are potential indicators of lung cancer. Simultaneously, we will employ the kNN algorithm in a novel application of unsupervised learning to cluster CT scan images based on the similarity of detected lung tumors, enabling the identification of patterns and characteristics that may correlate with specific types of lung cancer. This project involves collecting and preprocessing a diverse dataset of CT images annotated with radiologist insights to train the YOLO-v5 model. Subsequently, the kNN algorithm will be applied to perform clustering on the detected tumors. By achieving high accuracy in nodule detection and effectively clustering similar tumors, the system aims to become an invaluable tool for radiologists, providing rapid diagnostic assistance and facilitating a deeper understanding of lung cancer characteristics.

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.

Effect of emphysema on AI software and human reader performance in lung nodule detection from low-dose chest CT

BackgroundEmphysema influences the appearance of lung tissue in computed tomography (CT). We evaluated whether this affects lung nodule detection by artificial intelligence (AI) and human readers (HR).MethodsIndividuals were selected from the “Lifelines” cohort who had undergone low-dose chest CT. Nodules in individuals without emphysema were matched to similar-sized nodules in individuals with at least moderate emphysema. AI results for nodular findings of 30–100 mm3 and 101–300 mm3 were compared to those of HR; two expert radiologists blindly reviewed discrepancies. Sensitivity and false positives (FPs)/scan were compared for emphysema and non-emphysema groups.ResultsThirty-nine participants with and 82 without emphysema were included (n = 121, aged 61 ± 8 years (mean ± standard deviation), 58/121 males (47.9%)). AI and HR detected 196 and 206 nodular findings, respectively, yielding 109 concordant nodules and 184 discrepancies, including 118 true nodules. For AI, sensitivity was 0.68 (95% confidence interval 0.57–0.77) in emphysema versus 0.71 (0.62–0.78) in non-emphysema, with FPs/scan 0.51 and 0.22, respectively (p = 0.028). For HR, sensitivity was 0.76 (0.65–0.84) and 0.80 (0.72–0.86), with FPs/scan of 0.15 and 0.27 (p = 0.230). Overall sensitivity was slightly higher for HR than for AI, but this difference disappeared after the exclusion of benign lymph nodes. FPs/scan were higher for AI in emphysema than in non-emphysema (p = 0.028), while FPs/scan for HR were higher than AI for 30–100 mm3 nodules in non-emphysema (p = 0.009).ConclusionsAI resulted in more FPs/scan in emphysema compared to non-emphysema, a difference not observed for HR.Relevance statementIn the creation of a benchmark dataset to validate AI software for lung nodule detection, the inclusion of emphysema cases is important due to the additional number of FPs.Key points• The sensitivity of nodule detection by AI was similar in emphysema and non-emphysema.• AI had more FPs/scan in emphysema compared to non-emphysema.• Sensitivity and FPs/scan by the human reader were comparable for emphysema and non-emphysema.• Emphysema and non-emphysema representation in benchmark dataset is important for validating AI.Graphical

Endometriosis MRI: Atypical cases, pitfalls and mimics.

Endometriosis is a common but often underdiagnosed chronic gynaecological disease. Endometriosis mimics other diagnoses both clinically and radiographically, presenting a diagnostic challenge. Endometriosis can be categorised as superficial pelvic endometriosis, deep invasive endometriosis (DIE) or endometrioma. Endometrioma and DIE, including polypoid endometriosis, can masquerade as invasive neoplasms. Endometriosis can be misdiagnosed in less common locations or during pregnancy. Ultrasound is the initial investigation for endometriosis; however, MRI is advantageous in providing a larger field of view for increased detection of nodules as well as distinguishing malignancy with greater certainty. This review highlights endometriosis mimics, pitfalls and atypical cases on MRI.

Assessing Lateralization Index of Adrenal Venous Sampling for Surgical Indication in Primary Aldosteronism.

Clinical practice guidelines recommend the Lateralization Index (LI) as the standard for determining surgical eligibility in primary aldosteronism (PA). Our goal was to identify the optimal LI cut-offs in adrenal venous sampling (AVS) for diagnosing PA that is amenable to surgical cure. We conducted a retrospective international cohort study across 16 institutions in 11 countries, including 1,550 patients with PA who underwent AVS, with and/or without ACTH stimulation. The establishment of optimal cut-offs was informed by a survey of 82 PA patients in Japan, aimed at determining the LI cut-off aligned with patient expectations for a surgical cure rate. The survey revealed that a median cure rate expectation of 80% would motivate PA patients towards undergoing adrenalectomy. The optimal LI cut-offs achieving an adjusted positive predictive value (PPV) of 80% were identified as 3.8 for unstimulated AVS and 3.4 for ACTH-stimulated AVS. Furthermore, a contralateral ratio of less than 0.4 and the detection of an adrenal nodule on CT imaging were identified as independent predictors of surgically curable PA. Incorporating these factors with the optimal LI cut-offs, the adjusted PPV increased to 96.6% for unstimulated AVS and 89.6% for ACTH-stimulated AVS. No clear differences in predictive ability between unstimulated and ACTH-stimulated LI were found. The present study clarified the optimal LI cut-offs for without and with ACTH stimulation. The presence of contralateral suppression and adrenal nodule on CT imaging seems to provide additional available information besides LI for surgical indication.

Analysis of Lung Nodules and Lung Cancer Detection by Low-Dose Spiral CT of The Chest During Health Check-Up

Objective: To analyze the detection of lung nodules and lung cancer by low-dose spiral computed tomography (CT) of the chest in the population during health checkups. Methods: A total of 1,000 cases were selected for the data study and the data were retrospectively analyzed to detect lung nodules. They were divided into groups according to age, gender, and size of nodules, and the detection of lung nodules and lung cancer was compared. Results: A total of 268 lung nodules were detected, and the detection rates between genders were not significant (P > 0.05). The detection rate of lung nodules increased with age and stabilized after 60 years. When analyzing the size of lung nodules, there were 65 cases of nodules over 8mm. The patients were followed up for 18 months, with 36 cases of visits indicating lung cancer. A total of 10 cases were detected, including 7 cases of adenocarcinoma. Conclusion: Low-dose spiral CT chest examination during checkups can detect lung cancer early so that high-risk lung nodules can be dealt with promptly. Underlying lung adenocarcinoma can also be discovered. Individuals more than 40 years of age should regularly conduct low-dose spiral CT chest examinations to detect the presence of lung nodules or lung cancer as early as possible.

Analysis of the Prevalence Characteristics of Lung Nodules and Their Influencing Factors in The Health Check-Up Population

Objective: To study the prevalence characteristics of pulmonary nodules and their influencing factors in the health check-up population. Methods: A total of 500 cases of health checkups were selected for the data study to analyze the detection and prevalence characteristics of pulmonary nodules. The influencing factors between the pulmonary nodules group and the no pulmonary nodules group were analyzed. Results: A total of 209 cases were detected, of which there were more males than females, and for female patients, the detection rate increased with age (P < 0.05). The distribution of gender, age, smoking, respiratory symptoms, and exposure to kitchen fumes between the two groups was compared (P < 0.05). The multifactorial analysis concluded that risk factors include, respiratory symptoms, older age, exposure to kitchen fumes, and smoking. In addition, patients who exercised more had a lower risk of developing lung nodules. Conclusion: The detection rate of pulmonary nodules was high in the health check-up population and the influencing factors analyzed involved the presence of respiratory symptoms, older age, kitchen fume exposure, and smoking, while the positive influencing factor was increased exercise.

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.

Objective Comparison of White Light and Narrow-Band Imaging for Detecting Scars, Sulci and Nodules.

Narrow-band imaging (NBI) can improve detection of lesions in the aerodigestive tract. However, its role in benign lesions of the larynx is unclear. This study aims to determine whether NBI improves the detection of scars, sulci, and nodules compared to panchromatic lighting using objective image analysis. In total, 120 vocal folds (VFs) were analyzed with and without NBI (21 normal, 15 scars, 16 sulci, and 45 nodules). Each VF image had anterior, middle, and posterior thirds analyzed for brightness using an area morphometry software (Optimas 5.1a). The middle-third with the lesion was analyzed against surrounding VF segments for average and standard deviation (SD) in absolute grayscale. The use of panchromatic light resulted in greater illumination and grayscale values than NBI. All lesions tended to be in the mid-membranous fold. Under panchromatic light, change in brightness when comparing anterior versus middle (A-M) was +6.1% for normal, versus 6.5%, 8.1%, and 7.1% for sulci, nodules, and scars, respectively. Under NBI, they were 9.0% (normal), 12.3% (sulci), 13.7% (nodules), and 13.1% (scars). A greater SD of luminescence was observed at pathology sites (p < 0.05) when using NBI. The change in absolute grayscale at all lesion sites was greater when using NBI than when using panchromatic light (p < 0.05). NBI significantly enhanced the area of pathology in patients with nodules, sulci, and scars. Greater SD values in grayscale at pathologic sites were observed compared at normal sites. Thus, NBI may improve the detection of phonotraumatic lesions compared to panchromatic light. N/A Laryngoscope, 134:4066-4070, 2024.

Machine learning to predict the occurrence of thyroid nodules: towards a quantitative approach for judicious utilization of thyroid ultrasonography.

Ultrasound is instrumental in the early detection of thyroid nodules, which is crucial for appropriate management and favorable outcomes. However, there is a lack of clinical guidelines for the judicious use of thyroid ultrasonography in routine screening. Machine learning (ML) has been increasingly used on big data to predict clinical outcomes. This study aims to leverage the ML approach in assessing the risk of thyroid nodules based on common clinical features. Data were sourced from a Chinese cohort undergoing routine physical examinations including thyroid ultrasonography between 2013 and 2023. Models were established to predict the 3-year risk of thyroid nodules based on patients' baseline characteristics and laboratory tests. Four ML algorithms, including logistic regression, random forest, extreme gradient boosting, and light gradient boosting machine, were trained and tested using fivefold cross-validation. The importance of each feature was measured by the permutation score. A nomogram was established to facilitate risk assessment in the clinical settings. The final dataset comprised 4,386 eligible subjects. Thyroid nodules were detected in 54.8% (n=2,404) individuals within the 3-year observation period. All ML models significantly outperformed the baseline regression model, successfully predicting the occurrence of thyroid nodules in approximately two-thirds of individuals. Age, high-density lipoprotein, fasting blood glucose and creatinine levels exhibited the highest impact on the outcome in these models. The nomogram showed consistency and validity, providing greater net benefits for clinical decision-making than other strategies. This study demonstrates the viability of an ML-based approach in predicting the occurrence of thyroid nodules. The findings highlight the potential of ML models in identifying high-risk individuals for personalized screening, thereby guiding the judicious use of ultrasound in this context.

Enhanced thyroid nodule segmentation through U-Net and VGG16 fusion with feature engineering: A comprehensive study

Background and ObjectiveThe thyroid gland, a key component of the endocrine system, is pivotal in regulating bodily functions. Thermography, a non-invasive imaging technique utilizing infrared cameras, has emerged as a diagnostic tool for thyroid-related conditions, offering advantages such as early detection and risk stratification. Artificial intelligence (AI) has demonstrated success in medical diagnostics, and its integration into thermal imaging analysis holds promise for improving diagnostic capabilities. This study aims to explore the potential of AI, specifically convolutional neural networks (CNNs), in enhancing the analysis of thyroid thermograms for the detection of nodules and abnormalities. MethodsArtificial intelligence (AI) and machine learning techniques are integrated to enhance thyroid thermal image analysis. Specifically, a fusion of U-Net and VGG16, combined with feature engineering (FE), is proposed for accurate thyroid nodule segmentation. The novelty of this research lies in leveraging feature engineering in transfer learning for the segmentation of thyroid nodules, even in the presence of a limited dataset. ResultsThe study presents results from four conducted studies, demonstrating the efficacy of this approach even with a limited dataset. It's observed that in study 4, using FE has led to a significant improvement in the value of the dice coefficient. Even for the small size of the masked region, incorporating radiomics with FE resulted in significant improvements in the segmentation dice coefficient. It's promising that one can achieve higher dice coefficients by employing different models and refining them. ConclusionThe findings here underscore the potential of AI for precise and efficient segmentation of thyroid nodules, paving the way for improved thyroid health assessment.

Progression of Nodular Thyroid Disease in Familial Adenomatous Polyposis Syndrome: Refined Surveillance Recommendations

BackgroundPatients with familial adenomatous polyposis (FAP) have an increased risk of thyroid nodular disease. Previous studies demonstrated that screening thyroid ultrasound (US) will allow detection of nodules in 38% and thyroid cancer in 2.6% of patients. The aim of this study is to define the value of serial US evaluation at identifying disease progression in patients with FAP. MethodsRetrospective review from 2008 to 2023 at a single referral center. All patients with FAP and screening thyroid US were included. Patient demographics, initial US characteristics, follow-up regarding the development of new nodules and cancer were assessed using a Kaplan-Meier analysis. ResultsA total of 556 patients underwent screening. Fifty percent were male. Median age at first screening was 38 year old. Eighty percent underwent longitudinal follow-up for a median length of 7 years. At initial screening, 169 patients (30%) had nodules. For patients with normal baseline US, 14% developed a nodule overtime. A total of 20 patients (3.6%) were diagnosed with thyroid cancer. The cumulative incidence of initial and subsequent cancer was 4% by 5 years and 6% by 10 years, while the cumulative incidence of thyroid nodules was 40% and 48%, respectively. ConclusionsBased on the Kaplan-Meier analysis, ongoing longitudinal screening is warranted for patients with FAP as they are prone to thyroid cancer and nodule development overtime even when presenting with a baseline normal US. Additionally, these data demonstrate a slow development of thyroid cancer from a normal US, thus it is reasonable to consider selectively extending the screening interval for this population.

Semi-Supervised Thyroid Nodule Detection in Ultrasound Videos.

Deep learning techniques have been investigated for the computer-aided diagnosis of thyroid nodules in ultrasound images. However, most existing thyroid nodule detection methods were simply based on static ultrasound images, which cannot well explore spatial and temporal information following the clinical examination process. In this paper, we propose a novel video-based semi-supervised framework for ultrasound thyroid nodule detection. Especially, considering clinical examinations that need to detect thyroid nodules at the ultrasonic probe positions, we first construct an adjacent frame guided detection backbone network by using adjacent supporting reference frames. To further reduce the labour-intensive thyroid nodule annotation in ultrasound videos, we extend the video-based detection in a semi-supervised manner by using both labeled and unlabeled videos. Based on the detection consistency in sequential neighbouring frames, a pseudo label adaptation strategy is proposed for the refinement of unpredicted frames. The proposed framework is validated on 996 transverse viewed and 1088 longitudinal viewed ultrasound videos. Experimental results demonstrated the superior performance of our proposed method in the ultrasound video-based detection of thyroid nodules.

Lung Cancer Detection Using CNN VGG19 + Model

In the last many years, lung cancer has become a major public health concern. To examine cell breakdown in the lungs in its starting stages, doctors often use imaging modalities such as X-ray chest films, CT scans, MRIs, etc. The timing of diagnosis determines the course of therapy. Artificial intelligence (AI) is a hotspot for developing computational models of human intellect. In this research, we aim to enhance the detection and classification of lung nodules from CT images using a novel deep learning approach. Our study builds upon an extensive review of existing lung cancer detection methods, highlighting their strengths and weaknesses. This LIDC/IDRI dataset has been used in over half of the most recent research on lung cancer diagnosis. While several Convolutional Neural Networks (CNNs) architecture are adequate to process medical field data using technology solutions to discover and diagnose Lung cancer, here we proposed model VGG-19+ model. It is known for its strong feature extraction capabilities. It outflanks the ongoing model on a few exhibition measures, including exactness, accuracy, responsiveness/review, and f1-score.This research using VGG-19+ model could lead to more widespread utilization in cancer diagnosis by enhancing early lung cancer detection and developing the field of medical image analysis.

Accuracy Study on Deep Learning-Based CT Image Analysis for Lung Nodule Detection and Classification

Accuracy Study on Deep Learning-Based CT Image Analysis for Lung Nodule Detection and Classification

Pulmonary Nodule Segmentation Network Based on Res Select Kernel Contextual U-Net

Abstract U-Net network is widely used in the field of medical image segmentation. The automatic segmentation and detection of lung nodules can help in the early detection of lung cancer. Therefore, in this paper, to solve the problems of small proportion of nodules in computer tomography (CT) images, complex features, and insufficient segmentation accuracy, an improved U-Net network based on residual network and attention mechanism was proposed. The feature extraction part of Res select Kernel Contextual U-Net (RkcU-Net) network is based on Res2net, a variant of Resnet, and on which a feature extraction module with automatic selection of convolution kernel size is designed to perform multiscale convolution inside the feature layer to form perceptual fields of different sizes. This module selects the appropriate convolution kernel size to extract lung nodule features in the face of different fine-grained lung nodules. Second, the contextual supplementary (CS) block is designed to use the information of adjacent upper and lower layers to correct for the upper layer features, eliminating the discrepancy in the fusion of features at different levels. In this paper, the LUNA16 dataset was selected as the basis for lung nodule segmentation experiments. The method used in this dataset can achieve an intersection ratio (IoU) of 80.59% and a dice similarity coefficient (DSC) score of 89.25%. The network effectively improves the accuracy of lung nodule segmentation compared with other models. The results show that the method enhances the feature extraction ability of the network and improves the segmentation effect. In addition, the contribution of jump connections to information recovery should be noted.

Cross-spectral vision transformer for lung nodule detection with improved moth flame algorithm using deep learning

The second-leading cause of cancer-related deaths globally is lung cancer. A patient's life may be saved if this cancer is discovered early. Cancer can be difficult to detect on computed tomography (CT) images, according to physicians. Strong nodule segmentation is a difficulty because to the variety of pulmonary nodules and the visual similarities between nodules and their surroundings. Preprocessing, feature extraction, and classification are the three phases of the approach. Gather databases first from open source platforms. Unwanted noise in acquired CT images reduces the effectiveness of classification. In order to remove undesired noise from the input image, preprocessing techniques like filtering and contrast enhancement might be taken into consideration. The essential segmentation of the image is then carried out using optimization approaches like Automatic Lung Parenchyma Mining and Remora Concavity (ALPM & RC). Cross Spectral Visual Transformer and Improved Moth Flame Optimization (CSViT-IMFO) are the two classifiers that are being suggested. For multi-process optimization in CSViT, like structural and hyperparameter optimization, IMFO is used. Utilizing performance matrices like accuracy, precision, recall, specificity, sensitivity, and F_Measure, the proposed method is put into practice in MATLAB. On the LIDC dataset, our suggested solution outperforms cutting-edge learning-based methods with a classification accuracy of 99.5 %.

Semi-supervised lung nodule detection with adversarial learning

Semi-supervised lung nodule detection with adversarial learning

Development and Validation of an Algorithm Model for Predicting Heat Sink Effects during Pulmonary Thermal Ablation

The aim of this study was to develop an algorithm model to predict the heat sink effect during thermal ablation of lung tumors and to assist doctors in the formulation and adjustment of surgical protocols. The heat sink effect is an important factor affecting the therapeutic effect of tumor thermal ablation. At present, there is no algorithm model to predict the intraoperative heat sink effect automatically, which needs to be measured manually, which lacks accuracy and consumes time. To construct a segmentation model based on a convolutional neural network that can automatically identify and segment pulmonary nodules and vascular structure and measure the distance between the nodule and vascular. First, the classical Faster RCNN model was used as the nodule detection network. After obtaining the bounding box of pulmonary nodules, the VSPP-NET model was used to segment nodules in the bounding box. The distance from the nodule to the vasculature was measured after the surrounding vasculature was segmented by the VSPP-NET model. The lung CT images of 392 patients with pulmonary nodules were used as the training data for the algorithm. 68 cases were used as algorithm validation data, 29 as nodule algorithm test data, and 80 as vascular algorithm test data. We compared the heat sink effect of 29 cases of data with the results of the algorithm model and expert segmentation and compared the difference between the two results. In pulmonary CT image vasculature segmentation, the recall and precision of the algorithm model reached >0.88 and >0.78, respectively. The average time for automatic segmentation of each image model is 29 seconds, and the average time for manual segmentation is 158 seconds. The output image of the model shows that the results of nodule segmentation and nodule distance measurement are satisfactory. In terms of heat sink effect prediction, the positive rate of the algorithm group was 28.3%, and that of the expert group was 32.1%, with no significant difference between the two groups (p=0.687). The algorithm model developed in this study shows good performance in predicting the heat sink effect during pulmonary thermal ablation. It can improve the speed and accuracy of nodule and vessel segmentation, save ablation planning time, reduce the interference of human factors, and provide more reference information for surgeons to make ablation plans to improve the ablation effect.

Transformation with Yolo Tiny Network Architecture for Multimodal Fusion in Lung Disease Classification

This article focuses on the crucial task of identifying lung diseases through the analysis and processing of medical images, aiming to aid medical professionals in the diagnostic process. Image fusion technologies, employed in prospective remedies, enhance diagnostic precision and reliability by amalgamating radiological information from diverse sources. The study introduces a Yolo Tiny network-based medical imaging fusion technique, yielding fused images with enhanced clarity and diverse physical information. The proposed method involves the integration of an adaptive pre-processing module, YOLO Tiny for efficient classification, discrete wavelet transform for feature combination, and a novel adaptive image fusion technique based on a classifier, contributing to improved diagnostic accuracy. Addressing challenges like uneven illumination and poor contrast in medical images, the comprehensive workflow enhances image quality, facilitating effective classification in medical imaging. Experimental results showcase the efficacy of the proposed scheme in CT imaging signs retrieval and classification, specifically for lung nodule detection. The system exhibits high accuracy, specificity, sensitivity, and recall, outperforming existing systems in terms of SSIM, PSNR, MSE, and overall accuracy for both single-modal and multi-modal datasets. The incorporation of YOLO and YOLO Tiny networks underscores improved performance in image processing and classification tasks across diverse medical imaging modalities. The hybrid model, suggested in this study, effectively preserves rich specific information from source images, yielding esthetically pleasing visuals for medical practitioners and enhancing the diagnostic process.