Classification of Chest CT Lung Nodules Using Collaborative Deep Learning Model

Khalaf Alshamrani,1,2 Hassan A Alshamrani1 1Radiological Sciences Department, Najran University, Najran, Saudi Arabia; 2Department of Oncology and Metabolism, University of Sheffield, Sheffield, UKCorrespondence: Khalaf Alshamrani, Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK, Email [email protected]; [email protected]: 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.Methods: This study introduces a model based on collaborative deep learning (CDL) to differentiate between cancerous and non-cancerous 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.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.Keywords: CT images, lung cancer, nodules, logistic regression, collaborative deep learning, standard deviation, radial lengt

Prevalence of and Risk Factors for Skin Picking Disorder Symptoms Among Adults in an Arab Middle Eastern Population: A Cross-Sectional Study

Hazim Abdulkarim Khatib,1 Waleed Ahmed Alghamdi,2 Ahmed Hussein Subki,3 Nadeem Shafique Butt,4 Mohammed Saad Alsallum,5 Ahmed Salem Alsulaimani,6 Sara Faisal Alnajjar,7 Fahad Daifallah Alzaidi,8 Abdulrahman Ali Alasmari,9 Hussein Mesfer Alshamrani,10 Faten Al-Zaben,2 Harold G Koenig2,11,12 1Department of Internal Medicine, King Abdulaziz Hospital, Jeddah, Saudi Arabia; 2Department of Psychiatry, King Abdulaziz University, Jeddah, Saudi Arabia; 3Department of Internal Medicine, King Faisal Specialist Hospital & Research Centre, Jeddah, Saudi Arabia; 4Department of Community Medicine, King Abdulaziz University, Jeddah, Saudi Arabia; 5Department of Neurology, King Abdulaziz Medical City, Jeddah, Saudi Arabia; 6Department of Emergency Medicine, King Fahad Medical City, Riyadh, Saudi Arabia; 7Department of Diagnostic Radiology, King Abdulaziz University Hospital, Jeddah, Saudi Arabia; 8Department of Internal Medicine, King Abdulaziz University Hospital, Jeddah, Saudi Arabia; 9Department of Forensic Medicine, Forensic Medicine Center, Jeddah, Saudi Arabia; 10Department of Dermatology, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia; 11School of Public Health, Ningxia Medical University, Yinchuan, People’s Republic of China; 12Department of Psychiatry, Duke University Medical Center, Durham, NC, USACorrespondence: Waleed Ahmed Alghamdi; Harold G Koenig, Email [email protected]; [email protected]: Skin Picking Disorder (SPD) is a skin-related disease, also recognized as psychogenic excoriation, dermatillomania, or excoriation disorder. SPD is defined as a habitual picking of skin, which in turn harms skin tissue. Given the paucity of information on SPD symptoms, their prevalence, and risk factors in Saudi Arabia, the present study seeks to fill this gap by investigating these factors in a community sample from Jeddah.Methods: This descriptive cross-sectional study was conducted in the city of Jeddah. The Skin Picking Scale-Revised (SPS-R) scale was administered to a convenience sample of 520 respondents. A partial least squares path model (PLS-PM) for “impairment” and “symptoms severity” subscales was assessed by evaluating the validity of measurement and structural models.Results: Skin picking behavior was reported by 28.8% (n=150). A significant level of skin picking disorder symptoms was present in 1.2% (n=6). Skin picking visual effect, depressive symptoms, and being unmarried were the only positive independent predictors of the total SPS-R score.Conclusion: SPD symptoms are relatively common among the adult population in Jeddah, but those with threshold symptoms indicative of SPD are relatively few. Such behavior is particularly common in vulnerable groups such as those with depressive symptoms and the unmarried. More attention to this condition by clinicians will improve the quality of life of those affected, and reduce the emotional and physical health consequences of this often unrecognized condition.Keywords: skin picking disorder, dermatillomania, excoriation disorder, prevalence, risk factors, The Skin Picking Scale-Revised scal