Image Database Research Articles

G2NPAN: GAN-guided nuance perceptual attention network for multimodal medical fusion image quality assessment

Multimodal medical fusion images (MMFI) are formed by fusing medical images of two or more modalities with the aim of displaying as much valuable information as possible in a single image. However, due to the different strategies of various fusion algorithms, the quality of the generated fused images is uneven. Thus, an effective blind image quality assessment (BIQA) method is urgently required. The challenge of MMFI quality assessment is to enable the network to perceive the nuances between fused images of different qualities, and the key point for the success of BIQA is the availability of valid reference information. To this end, this work proposes a generative adversarial network (GAN) -guided nuance perceptual attention network (G2NPAN) to implement BIQA for MMFI. Specifically, we achieve the blind evaluation style via the design of a GAN and develop a Unique Feature Warehouse module to learn the effective features of fused images from the pixel level. The redesigned loss function guides the network to perceive the image quality. In the end, the class activation mapping supervised quality assessment network is employed to obtain the MMFI quality score. Extensive experiments and validation have been conducted in a database of medical fusion images, and the proposed method is superior to the state-of-the-art BIQA method.

From Data to Design: Constructing Scapula and Hip Bone Through Online Datasets, Open-Source Software and 3D Printers.

Introduction Human skeletons and bones are essential for medical and allied sciences students. Nowadays, it is becoming challenging to procure bone legally, resulting in medical students' inadequacy of bone. Plaster-of-Paris or resin bone models sold on the market are less detailed than real bones. Aims and objectives This study aims to create a three-dimensional (3D)-printed human bone model with free, open-source software and fused deposition modelling (FDM) 3D printers, compare its accuracy with the original bones and validate it with a textbook description. Methods Scapula and hip bone models were produced using open-source software 3D Slicer from computed tomography (CT) data from the "New Mexico Decedent Image Database". After automated segmentation, bones were edited manually with tools in a 3D Slicer(https://www.slicer.org/) and Meshmixer software(Autodesk Inc., San Rafael, California, United States) and 3D bone models were printed using polylactic (PLA) filament. Results and discussions 3Ddigital models of both bones were successfully created with the maximum possible accuracy with an FDM 3D printer. A 3D digital replica of the scapula obtained after segmentation retained most anatomical features except for the glenoid cavity, as the head of the humerus obscured the glenoid cavity. The 3D digital skeleton of the hip bone retained all anatomical features except articulating surfaces, such as the acetabulum and auricular surface ilium, which were obscured by the head of the femur and sacrum. A few morphological features of both bones differed from the original dry bone, and a few finer details were unclear in the iliac fossa and ischium. After manual editing and post-processing, the final physical model obtained has all the features. Conclusions We conclude that it is possible to produce anatomically accurate models with the greatest possible resemblance and accuracy to the original bones using free and open-source data with an FDM 3D printer.

Blockchain with optimal deep learning assisted secure data sharing and classification on future healthcare systems

In the future of healthcare, Blockchain (BC) technology holds immense potential for improving the security and privacy of data. By allowing the secure and immutable storage of medical files and healthcare-related transactions, BC ensured that sensitive medical data remains tamper-proof and open only to authorized parties. Patients have greater control over their data's development, revoking or granting access as required, but healthcare workers can streamline data sharing and ensure the integrity of important data. The decentralized nature of BC networks decreases the risk of centralized data breaches, eventually fostering trust and transparency in the healthcare ecosystems. Conversely, deep learning (DL) has great to revolutionize healthcare diagnostics in the future, offering quick and extremely accurate estimates of medical conditions. This technology has greatly enhanced patient solutions, decreased medical expenses, and improved the burden on medical staff by providing appreciated insights into an extensive range of conditions, from cancer to neurological disorders. With this stimulus, this study presents a novel BC with optimal DL-based secure data sharing and classification (BCODL-SDSC) technique in the future healthcare system. The goal of the BCODL-SDSC technique is to secure and thoroughly examine healthcare data using BC and DL techniques. Primarily, the BCODL-SDSC technique enables BC technology to store and maintain the patient’s data from the procedure of several transactions and enable access control to the various stakeholders. For the security of the medical images, the BCODL-SDSC technique applies the Fractional Order Lorenz system (FOLS) based encryption technique with tuna swarm optimization (TSO) algorithm based optimal key generation process. Finally, a multi-stage process performs the classification of the medical images: MobileNetv1 feature extractor, artificial rabbit’s optimization (ARO) based hyperparameter tuning, and stacked recurrent neural network (SRNN) based classification. The experimental outcome of the BCODL-SDSC technique was examined on a benchmark medical image database. An extensive comparative study reported that the BCODL-SDSC technique reaches an effective performance with other models with a maximum accuracy of 99.11%.

A methodology for phase characterization in pellet feed using digital microscopy and deep learning

This work proposes a new method for characterizing pellet feed, employing Deep Learning (DL) and Convolutional Neural Networks (CNNs). The main minerals in the composition of the studied pellet feed are hematite, magnetite, goethite, and quartz. Over time, several characterization methodologies have been developed that use Digital Microscopy and Image Analysis tools. The greatest difficulties in this characterization lie in differentiating the textures of hematite particles, the different shapes of their crystals, or discriminating between quartz and resin in reflected light optical microscopy images. This work proposes a mineral characterization methodology based on the Mask R-CNN algorithm. The goal is to perform instance segmentation, that is, to identify, classify, and segment objects in the images. Two DL models were combined: the BF Model performs instance segmentation for the compact, porous, martite, and goethite classes in images obtained in Bright Field mode, and the CPOL Model uses images acquired in Circularly Polarized Light to segment the monocrystalline, polycrystalline, and martite classes. An F1-score of around 80% was obtained for the BF Model and around 90% for the CPOL Model. The results were promising and can be improved as the training image database increases.

Lung cancer computed tomography image classification using Attention based Capsule Network with dispersed dynamic routing

AbstractLung cancer is relying as one of the significant and leading cause for the deaths which are based on cancer. So, an effective diagnosis is a crucial step to save the patients who are all dying due to lung cancer. Moreover, the diagnosis must be performed based on the severity of lung cancer and the severity can be addressed with the help of an optimal classification approach. So, this research introduced an Attention based Capsule Network (A‐Caps Net) with dispersed dynamic routing to perform in‐depth classification of the disease affected partitions of the image and results in better classification results. The attention layer with dispersed dynamic routing evaluates the digit capsule from feature vector in a constant manner. As the first stage, data acquisitioned from datasets such as Lung Nodule Analysis‐16 (LUNA‐16), The Cancer Imaging Archive (TCIA) dataset and Lung Image Database Consortium and Image Database Resource Initiative (LIDC‐IDRI). After acquisitioning data, pre‐processing is done to enhance the resolution of the image using Generative Adversarial Network. The pre‐processed output is given as output for extraction of features that takes place using GLCM and VGG‐16 which extracts the low level features and high level features respectively. Finally, categorization of lung cancer is performed using Attention based Capsule Network (A‐Caps Net) with dispersed dynamic routing which categorize the lung cancer as benign and malignant. The results obtained through experimental analysis exhibits that proposed approach attained better accuracy of 99.57%, 99.91% and 99.29% for LUNA‐16, LIDC‐IDRI and TCIA dataset respectively. The classification accuracy achieved by the proposed approach for LUNA‐16 dataset is 99.57% which is comparably higher than DBN, 3D CNN, Squeeze Nodule Net and 3D‐DCNN with multi‐layered filter with accuracies of 99.16%, 97.17% and 94.1% respectively.

Content Based Image Retrieval Using Support Vector Machine

A significant issue in multimedia systems is image retrieval. It is identified as the procedure for looking through and obtaining pictures from a dataset. One important and difficult area of study in digital image processing is content-based image retrieval (CBIR). Retrieving pertinent data from a vast picture database after a query image with better system output is a fundamental requirement of the CBIR system. Regretfully, not all techniques can be applied to achieve high retrieval accuracy. Thus, the purpose of this research is to use Support Vector Machine (SVM) to classify query picture data with image database data in order to get similar images. Based on the classification, the accuracy of the image retrieval will be verified.

Automatic geometry-based estimation of the locus coeruleus region on T1-weighted magnetic resonance images

The locus coeruleus (LC) is a key brain structure implicated in cognitive function and neurodegenerative disease. Automatic segmentation of the LC is a crucial step in quantitative non-invasive analysis of the LC in large MRI cohorts. Most publicly available imaging databases for training automatic LC segmentation models take advantage of specialized contrast-enhancing (e.g., neuromelanin-sensitive) MRI. Segmentation models developed with such image contrasts, however, are not readily applicable to existing datasets with conventional MRI sequences. In this work, we evaluate the feasibility of using non-contrast neuroanatomical information to geometrically approximate the LC region from standard 3-Tesla T1-weighted images of 20 subjects from the Human Connectome Project (HCP). We employ this dataset to train and internally/externally evaluate two automatic localization methods, the Expected Label Value and the U-Net. For out-of-sample segmentation, we compare the results with atlas-based segmentation, as well as test the hypothesis that using the phase image as input can improve the robustness. We then apply our trained models to a larger subset of HCP, while exploratorily correlating LC imaging variables and structural connectivity with demographic and clinical data. This report provides an evaluation of computational methods estimating neural structure.

An Ensemble Approach To Face Recognition In Access Control Systems

An Ensemble Approach To Face Recognition In Access Control Systems

QUANTIFYING RADIATION EXPOSURE IN THE RADIOLOGICAL INVESTIGATION OF NON-ARTHRITIC HIP PAIN

Radiological investigations are essential in the work-up of patients presenting with non-arthritic hip pain, to allow close review of the complex anatomy around the hip and proximal femur. The aim of this study is to quantify the radiation exposure associated with common radiological investigations performed in assessing young adult patients presenting with non-arthritic hip pain.A retrospective review of our UK tertiary hip preservation centre institutional imaging database was performed. Data was obtained for antero-posterior, cross-table lateral and frog-lateral radiographs, along with data for the low dose CT hip protocol and the Mako CT Hip protocol. The radiation dose of each imaging technique was measured in terms of dose-area product (DAP) with units of mGycm2, and the effective doses (ED, mSv) calculated.The mean effective radiation dose for hip radiographs was in the range 0.03 to 0.83mSv (mean DLP 126.7–156.2 mGycm2). The mean effective dose associated with the low-dose CT hip protocol was 3.04mSv (416.8 mGycm2) and for the Stryker Mako CT Hip protocol was 8.4mSv (1061 mGycm2). The radiation dose associated with use of CT imaging was significantly greater than plain radiographs (p<0.005)Investigation of non-arthritic hip pain can lead to significant ionising radiation exposure for patients. In our institution, the routine protocol is to obtain an anteroposterior radiograph and then a specific hip sequence 3 Tesla MRI including anteversion views. This provides the necessary information in the majority of cases, with CT scanning reserved for more complex cases where we feel there is a specific indication. We would encourage the hip preservation community to carefully consider and review the use of ionising radiation investigations.

Abstract PO4-20-01: A PROSPECTIVE PILOT STUDY OF MAMMOGRAPHIC PREDICTION OF WOMEN’S RISK (M-POWER) IN NIGERIA

Abstract Background There have been no sustained efforts at stemming the rising incidence and mortality from breast cancer in sub-Saharan African countries like Nigeria. Mammography is the gold standard for breast cancer screening and is associated with improved survival particularly among women 50 to 69. Reports from various parts of Nigeria show very low mammography screening uptake. Causes include lack of geographical access, financial issues (less than 5% of the population is covered by the National Health Insurance Scheme), lack of awareness and the increasing expertise gap from the continued brain drain, despite the already low number of radiologists in the country. Given these obstacles, our long-term goal is to devise a rational basis for identifying mammographic risk categories so that this resource can be deployed cost-effectively. We propose to carry out a 5-year prospective mammographic study in Nigerian women and to use machine learning to develop a risk prediction algorithm, so that following a single screening mammogram, the appropriate frequency of subsequent mammograms can be determined. Our immediate goal is to examine the feasibility of performing such a study. To this end, we have initiated a cross-sectional pilot study that will recruit up to 2000 women in multiple urban centers in Nigeria. Objective: To explore feasibility of creating a digital mammography image database representative of the radiological features of the breast of Nigerian women. Objective: To explore feasibility of creating a database that can be used to inform a predictive mammographic risk model for breast cancer and identify the appropriate population that will benefit from breast cancer screening in Nigeria. Design This is a prospective, open-label, cross-sectional pilot study in 2000 eligible women in Nigeria and will include a single screening mammography examination. The mammography images will be read by the study radiologist. The Pilot phase will determine the feasibility and aid in the design of the of the Extension phase in which up to 20,000 participants will undergo yearly mammography examination for 5 years. Incident cases of breast cancer during the study period will permit the development of deep learning algorithms for predicting the risk of breast cancer in this population. Main Eligibility Criteria: Inclusion Criteria All consecutive women aged ≥40 years and < 65 years presenting for screening mammography at the participating institutions will be eligible. Signed study-specific written informed consent. Exclusion Criteria Complaint of a focal dominant lump or a bloody or clear nipple discharge. History of breast implants. Any woman who is pregnant or has reason to believe that she might be pregnant. Statistical Methods Analyses of continuous data will be summarized using descriptive statistics where the following parameters will be reported: Number of observations, Number of missing observations, Mean, Median, Standard deviation (SD), Minimum (Min), Maximum (Max) Categorical data will be presented with absolute and relative frequency (n and %). Present Accrual and Target accrual Two hundred ninety-six women have been enrolled and screening digital mammographic images using GE Digital mammography equipment have been obtained on all participants at 2 study sites in 2 cities in Southern Nigeria. The target accrual is 2000 participants over 12 months. Conclusion Our preliminary experience to date suggests the feasibility of proceeding with a larger prospective study to define mammographic risk groups in Nigerian women which will permit the rational allocation of resources for breast cancer prevention activities in this underserved population. Contact Information: Dr Toyin Shonukan at tshonukan@yemanjacancercare.org Citation Format: Oluwatoyin Shonukan, Hyelakumi Ibrahim, Esther Udoh, Wahab Egbeolu, Kanyinsola Oyeyinka, Jonas Haggstrom. A PROSPECTIVE PILOT STUDY OF MAMMOGRAPHIC PREDICTION OF WOMEN’S RISK (M-POWER) IN NIGERIA [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-20-01.

Design And Development of Sketch Based Image Retrieval Using Deep Learning

In this cutting edge, the common wrong doing rate is expanding day-by-day and to manage up with this the criminal divisions as well ought to discover ways in which would speed up the by and large preparation and offer assistance in bringing one to justice. In response to rising crime rates, law enforcement agencies are turning to advanced algorithms capable of matching freehand sketches with images in databases. These algorithms, utilizing sophisticated feature extraction techniques and deep learning models, significantly enhance identification accuracy. By leveraging Sketch based image retrieval technology, investigations are expedited, leading to quicker suspect apprehensions and resolution of criminal cases. This results in improved public safety and justice outcomes, as well as more efficient law enforcement practices overall.

Automated Reservoir Characterization of Carbonate Rocks using Deep Learning Image Segmentation Approach

Summary The objective of this study is to develop a systematic and novel workflow for the automated and objective characterization of carbonate reservoirs with the help of deep learning architectures. An image database of more than 6,000 carbonate thin-section images was generated using the optical microscope and image augmentation techniques. Five features, namely clay/silt/mineral, calcite, pores, fossils, and opaque minerals, were identified with the help of manual petrography of the thin sections under the microscope. A total of four deep learning models were developed, which included U-Net, U-Net with ResNet34 backbone, U-Net with Mobilenetv2 backbone, and LinkNet with ResNet34 backbone. The Ensemble model of U-Net + ResNet34 and U-Net + MobileNetv2 yielded the highest intersection over union (IoU) score of 75%, followed by the U-Net + ResNet34 model with an IoU score of 61%. The models struggled with class imbalance, which was very prominent in the image database, with classes such as fossils and opaques considered to be rare. The statistical analysis of the relative errors revealed that the major classes play a more important role in increasing the final IoU score as opposed to the common understanding that the rare classes affect the model performance. The novel workflow developed in this paper can be extended to real carbonate reservoirs for time efficient, objective, and accurate characterization.

Lensless shadow microscopy-based shortcut analysis strategy for fast quantification of microplastic fibers released to water

Fast quantification is the primary challenge in monitoring microplastic fiber (MPF) pollution in water. The process of quantifying the number of MPFs in water typically involves filtration, imaging on a filter membrane, and manual counting. However, this routine workflow has limitations in terms of speed and accuracy. Here, we present an alternative analysis strategy based on our high-resolution lensless shadow microscope (LSM) for rapid imaging of MPFs on a chip and modified deep learning algorithms for automatic counting. Our LSM system was equipped with wide field-of-view submicron-pixel imaging sensors (>1 cm2; ∼500 nm/pixel) and could simultaneously capture the projection image of >3-μm microplastic spheres within 90 s. The algorithms enabled accurate classification and detection of the numbers and sizes of >7-μm linear and branched MPFs derived from melamine cleaning sponges in each image (∼0.4 gigapixels) within 60 s. Importantly, neither MPF morphology (dispersed or aggregated) nor environmental matrix had a notable impact on the automatic recognition of the MPFs by the algorithms. This new strategy had a detection limit of 10 particles/mL and significantly reduced the time of MPF imaging and counting from several hours with membrane-based methods to just a few minutes per sample. The strategy could be employed to monitor water pollution caused by microplastics if an efficient sample separation and a comprehensive sample image database were available.

Prevalence of accessory carpal ossicles - a CT-based survey

This observational study aimed to determine the prevalence of accessory carpal ossicles (ACO) using a computerized tomography examination. The digital imaging database of the authors’ institution was retrospectively reviewed, and all wrist computerized tomography examinations for any indication were identified between 2014 and 2021. Patients with previous wrist surgery and severely impaired carpal anatomy, such as severe osteoarthritis or congenital or acquired deformities that may preclude evaluation and identification of accessory carpal ossicles, were excluded from the study. Four orthopedic surgeons reviewed the computerized tomography scans and identified patients with accessory carpal ossicles on two-dimensional (coronal, axial, and sagittal planes) and three-dimensional computerized tomography reconstructions. Two thousand two hundred thirteen patients, with a mean age of 36.8±12.8 years, were identified and included. Accessory carpal ossicles were detected in 156 (7.1%) subjects, with 186 ACOs identified. The most common accessory carpal ossicles were os praetrapezium (n:34, 1.536%), os triquetrum secundarium (n:28, 1.265%), os epitriquetrum (n:20, 0.903%), os hamuli proprium (n:18, 0.813%), os ulnostyloideum (n:16, 0.723%), os epilunatum (n:12, 0.542%) and os styloideum (n:11, 0.497%) respectively. The study provides valuable information on the prevalence and distribution of accessory carpal ossicles, which can aid in accurately diagnosing and managing wrist pathologies. Further research is warranted to explore the clinical significance and potential impact of rare accessory carpal ossicles on wrist disorders.

Visual smoke recognition based on an inverse-radiating attention pyramid network

In thermal power generation systems, the exhaust gases produced during the process of power generation should be fully burnt before being discharged into the air through a high-altitude chimney, to help maintain safe production and to avoid pollution. A thermal power generation system will emit white smoke when operating normally; otherwise, under abnormal conditions: (1) large amounts of dark smoke may be released into the air due to incomplete combustion of the exhaust gases, leading to hazards to public health and the environment; (2) colorless smoke can be emitted if toxic exhaust gases are directly released into the air, also causing dangerous pollution. To address these issues, we have developed an inverse-radiating attention pyramid network (IRAP-Net) for visual smoke recognition, directed towards monitoring thermal power generation systems. The proposed IRAP-Net exploits various visual smoke features. IRAP-Net is based on three pyramid blocks, composed of 3, 4 and 5 convolution modules, respectively. An attention mechanism is introduced into each pyramid block selective of features. Lastly, an inverse-radiating connection converges all the outputs of the pyramid blocks in a feedforward manner, thereby merging low-level and high-level features. Experiments on a large smoke image database show that the proposed IRAP-Net achieves a recognition rate exceeding 95.5%, superior to other state-of-the-art (SOTA) models.

An Efficient Search on Cloud Images with Blockchain Technology

An Efficient Search on Cloud Images with Blockchain Technology

Распознавание дорожных знаков российского образца с использованием нейронных сетей

The problem of creating a model for recognizing objects in images and possible ways to solve it is considered using the example of working with Russian road signs according to ISS R 52290-2004. The analysis of methods for constructing predictive models of image recognition and existing solutions in the public domain is carried out. A convolutional neural network is used as the basic model. A road sign recognition model based on the YOLOv7 transfer network has been developed as a result of training on a dataset from the Russian RTSD road sign image database. The metrics for evaluating the quality of the created model are analyzed and described. The created model meets the quality requirements for objective metrics, allows you to make forecasts taking into account specific situations in different weather conditions and at different times of the day for 146 different predefined classes. The characteristic of the class is the number of the sign according to ISS R 52290-2004. The model has a prediction accuracy of 0.847 with a prediction completeness of 0.811. The average average prediction accuracy of the model is 0.884 when tested on 493 images from the test sample. The test sample does not overlap with the training sample, which consists of 1,842 images. The developed model is published in the public domain both for use for scientific purposes and for further further education. This provides an opportunity for researchers in this field to familiarize themselves with a practical example of the implementation of the model, to supplement or improve it if necessary. The method described in this paper will allow researchers in various subject areas to find a solution that allows them to overcome resource constraints when creating a high-performance and high-quality predictive recognition model.

Improved Watermarking Performance in Color Images through a hybrid of DWT-DCT Integration for Copyright Protection

The era of big data has changed the view of data, data can now be considered as data assets. The definition of data assets is data that has rights (exploration rights, use rights, and ownership rights). Some experts believe that data assets have value at their core, the value contained can be in the form of information owned by data assets. Sharing digital data is growing every day as more people access the Internet quickly. Unauthorized individuals can easily access multimedia, such as text, images, video and audio. This research focuses on digital image watermarking to ensure security and copyright protection. This scheme uses a watermarking technique based on the hybrid of the two transformation domains (frequency domain), Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT). The digital image is processed into several parts by the DWT, then the watermark is embedded into one of the parts in the frequency domain using the DCT transformation, then the parts are combined again in the DWT. The experimental results show that the watermarked images achieve the highest PSNR result was 45.2719 dB, and the lowest was 43.3194 dB. an average PSNR value of 44.1254 dB, by testing ten color image datasets sourced from the SIPI-USC image database.

Two-Phase Speckle Noise Removal in US Images: Speckle Reducing Improved Anisotropic Diffusion and Optimal Bayes Threshold

Medial images are contaminated by multiplicative speckle noise, which dramatically reduces ultrasound images and has a detrimental impact on a variety of image interpretation tasks. Hence, to overcome this issue, this paper presented a Two-Phase Speckle Reduction approach with Improved Anisotropic Diffusion and Optimal Bayes Threshold termed TPSR-IADOT, which includes the phases like image enhancement and two-level decomposition processes. Initially, the speckle noise is subjected to an image enhancement process where the Speckle Reducing Improved Anisotropic Diffusion (SRAID) filtering process is carried out for the speckle removal process. Afterwards, two-level decomposition takes place which utilizes Discrete Wavelet Transform (DWT) to remove the residual noise. As the speckle noise is mostly present in the high-frequency band, Improved Bayes Threshold will be applied to the high- frequency subbands. Finally, to provide the best outcomes, an optimization algorithm termed Self Improved Pelican Optimization Algorithm (SI-POA) in this work via choosing the optimal threshold value. The efficiency of the proposed method has been validated on an ultrasound image database using Simulink in terms of PSNR, SSIM, SDME and MAPE. Accordingly, from the analysis, it is proved that the proposed TPSR-IADOT attains the PSNR of 40.074, whereas the POA is 38.572, COOT is 38.572, BES is 37.003, PRO is 30.419, WOA is 33.218, RFU-LA is 29.935 and SSI-COA is 39.256, for noise variance[Formula: see text]0.1.

Advancing Pulmonary Nodule Diagnosis by Integrating Engineered and Deep Features Extracted from CT Scans

Enhancing lung cancer diagnosis requires precise early detection methods. This study introduces an automated diagnostic system leveraging computed tomography (CT) scans for early lung cancer identification. The main approach is the integration of three distinct feature analyses: the novel 3D-Local Octal Pattern (LOP) descriptor for texture analysis, the 3D-Convolutional Neural Network (CNN) for extracting deep features, and geometric feature analysis to characterize pulmonary nodules. The 3D-LOP method innovatively captures nodule texture by analyzing the orientation and magnitude of voxel relationships, enabling the distinction of discriminative features. Simultaneously, the 3D-CNN extracts deep features from raw CT scans, providing comprehensive insights into nodule characteristics. Geometric features and assessing nodule shape further augment this analysis, offering a holistic view of potential malignancies. By amalgamating these analyses, our system employs a probability-based linear classifier to deliver a final diagnostic output. Validated on 822 Lung Image Database Consortium (LIDC) cases, the system’s performance was exceptional, with measures of 97.84%, 98.11%, 94.73%, and 0.9912 for accuracy, sensitivity, specificity, and Area Under the ROC Curve (AUC), respectively. These results highlight the system’s potential as a significant advancement in clinical diagnostics, offering a reliable, non-invasive tool for lung cancer detection that promises to improve patient outcomes through early diagnosis.