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The approach decomposes nodules into constituent parts using six characteristics, employs a ResNet-50 fine-tuned CDL submodel with six feature patch types, and uses adaptive error-backpropagated weighting to improve nodule identification. The model achieves 93.24% accuracy, outperforming existing state-of-the-art methods and supporting timely 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Text Article ORIGINAL RESEARCH  \nClassification of Chest CT Lung Nodules Using Collaborative Deep Learning Model  \nKhalaf Alshamrani 1 , 2 , Hassan A Alshamrani 1  \n1Radiological Sciences Department, Najran University, Najran, Saudi Arabia; 2Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK  \nCorrespondence: Khalaf Alshamrani, Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK, Email [k.alshamrani@sheffield.ac.uk](k.alshamrani@sheffield.ac.uk); [kaalshamrani@hotmail.com](kaalshamrani@hotmail.com)  \n\n| Background: Early detection of lung cancer through accurate diagnosis of malignant lung nodules using chest CT scans offers patients the highest chance of successful treatment and survival. Despite advancements in computer vision through deep learning algorithms, the detection of malignant nodules faces significant challenges due to insufficient training datasets.\u003Cbr>Methods: This study introduces a model based on collaborative deep learning (CDL) to differentiate between cancerous and noncancerous nodules in chest CT scans with limited available data. The model dissects a nodule into its constituent parts using six characteristics, allowing it to learn detailed features of lung nodules. It utilizes a CDL submodel that incorporates six types of feature patches to fine-tune a network previously trained with ResNet-50 . An adaptive weighting method learned through error backpropagation enhances the process of identifying lung nodules, incorporating these CDL submodels for improved accuracy.\u003Cbr>Results: The CDL model demonstrated a high level of performance in classifying lung nodules, achieving an accuracy of 93.24% . This represents a significant improvement over current state-of-the-art methods, indicating the effectiveness of the proposed approach. Conclusion: The findings suggest that the CDL model, with its unique structure and adaptive weighting method, offers a promising solution to the challenge of accurately detecting malignant lung nodules with limited data. This approach not only improves diagnostic accuracy but also contributes to the early detection and treatment of lung cancer, potentially saving lives.\u003Cbr>Keywords: CT images, lung cancer, nodules, logistic regression, collaborative deep learning, standard deviation, radial length |\n| --- |\n| Introduction\u003Cbr>According to the 2015 Global Cancer Statistics, 13% of 14.1 million new cancer diagnoses and 19.5% of cancer deaths are lung cancer.1 Radiologist workload may be eased by advanced cancer patients’ five-year survival rate. Multi-categorizing different replies may alleviate this problem. Recent photo categorization algorithms include K-Mean, K-NN, deep neural networks, SVM, and others. Artificial neural networks are used for pattern categorization. Recently, academics have focused on the convolutional neural network (CNN), often called as.1 It provides accurate results in picture classification, recognition, language translation, medical diagnostics, and other domains. Also used in other areas.\u003Cbr>This research uses a convolutional neural network model called Multi-CNNs. This research gives model component output synthesis. We found a Multi-CNNs model workable in 96% of our x-ray picture dataset. Analyzing images led to these results.\u003Cbr>Deep learning dominates machine learning research.2 Deep learning has improved audio recognition, social network filtering, computer translation, and biology. Programs provide accurate outcomes.\u003Cbr>Many pattern recognition issues have been solved using CNN since its inception. These topics include computer vision, voice recognition, picture classification, identification, language translation, medical diagnostics, and others. CNNs filter and compress data into layers for clarity and use. These filters automatically update to extract the most important informatio","cbCaikZtKF1mdt42","https://ap.wps.com/l/cbCaikZtKF1mdt42","pdf",2081976,14,"English","# Background\n# Methods\n# Results\n# Conclusion\n# Introduction","[{\"question\":\"What problem does this study address in lung cancer detection?\",\"answer\":\"Accurate identification of malignant lung nodules on chest CT is difficult due to limited training datasets for deep learning models.\"},{\"question\":\"How does the proposed collaborative deep learning model work?\",\"answer\":\"It decomposes a nodule into parts using six characteristics and uses a CDL submodel with six types of feature patches to fine-tune a ResNet-50-based network, guided by adaptive error backpropagation weighting.\"},{\"question\":\"What performance did the CDL model achieve?\",\"answer\":\"The model achieved 93.24% accuracy in classifying lung nodules, showing improvement over current state-of-the-art methods.\"}]","Classification of Chest CT Lung Nodules Using Collaborative Deep Learning Model | PDF",1789996125,35]