The skull and pelvis have been used for the determination of sex for unknown human remains. However, in forensic cases where skeletal remains often exhibit postmortem damage and taphonomic changes the patella may be used for the determination of sex as it is a preservationally favoured bone. The goal of the present research was to derive discriminant function equations from the patella for estimation of sex from a contemporary Spanish population. Six parameters were measured on 106 individuals (55 males and 51 females), ranging in age from 22 to 85 years old, from the Granada Osteological Collection. The statistical analyses showed that all variables were sexually dimorphic. Discriminant function score equations were generated for use in sex determination. The overall accuracy of sex classification ranged from 75.2% to 84.8% for the direct method and 75.5%-83.8% for the stepwise method. When the South African White discriminant functions were applied to the Spanish sample they showed high accuracy rates for sexing female patellae (90%-95.9%) and low accuracy rates for sexing male patellae (52.7%-58.2%). When the South African Black discriminant functions were applied to the Spanish sample they showed high accuracy rates for sexing male patellae (90.9%) and low accuracy rates for sexing female patellae (70%-75.5%). The patella was shown to be useful for sex determination in the contemporary Spanish population.
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http://dx.doi.org/10.1016/j.jflm.2016.09.007 | DOI Listing |
Sci Rep
January 2025
Department of Biomedical Engineering, School of Life Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China.
The cervical cell classification technique can determine the degree of cellular abnormality and pathological condition, which can help doctors to detect the risk of cervical cancer at an early stage and improve the cure and survival rates of cervical cancer patients. Addressing the issue of low accuracy in cervical cell classification, a deep convolutional neural network A2SDNet121 is proposed. A2SDNet121 takes DenseNet121 as the backbone network.
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January 2025
College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China.
Breast cancer is one of the most aggressive types of cancer, and its early diagnosis is crucial for reducing mortality rates and ensuring timely treatment. Computer-aided diagnosis systems provide automated mammography image processing, interpretation, and grading. However, since the currently existing methods suffer from such issues as overfitting, lack of adaptability, and dependence on massive annotated datasets, the present work introduces a hybrid approach to enhance breast cancer classification accuracy.
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January 2025
Faculty of Engineering, Université de Moncton, Moncton, NB, E1A3E9, Canada.
Diabetes is a growing health concern in developing countries, causing considerable mortality rates. While machine learning (ML) approaches have been widely used to improve early detection and treatment, several studies have shown low classification accuracies due to overfitting, underfitting, and data noise. This research employs parallel and sequential ensemble ML approaches paired with feature selection techniques to boost classification accuracy.
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January 2025
Ministry of Higher Education, Mataria Technical College, Cairo, 11718, Egypt.
The current work introduces the hybrid ensemble framework for the detection and segmentation of colorectal cancer. This framework will incorporate both supervised classification and unsupervised clustering methods to present more understandable and accurate diagnostic results. The method entails several steps with CNN models: ADa-22 and AD-22, transformer networks, and an SVM classifier, all inbuilt.
View Article and Find Full Text PDFJ Med Ethics
January 2025
Warren Alpert Medical School, Brown University, Providence, Rhode Island, USA.
Introduction: The integration of artificial intelligence (AI) into healthcare introduces innovative possibilities but raises ethical, legal and professional concerns. Assessing the performance of AI in core components of the United States Medical Licensing Examination (USMLE), such as communication skills, ethics, empathy and professionalism, is crucial. This study evaluates how well ChatGPT versions 3.
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