Yazar "Alkurdi, Dunya Ahmed" seçeneğine göre listele

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    Advancing deepfake detection using Xception architecture: A robust approach for safeguarding against fabricated news on social media
    (Tech Science Press, 2024) Alkurdi, Dunya Ahmed; Çevik, Mesut; Akgundogdu, Abdurrahim
    Deepfake has emerged as an obstinate challenge in a world dominated by light. Here, the authors introduce a new deepfake detection method based on Xception architecture. The model is tested exhaustively with millions of frames and diverse video clips; accuracy levels as high as 99.65% are reported. These are the main reasons for such high efficacy: superior feature extraction capabilities and stable training mechanisms, such as early stopping, characterizing the Xception model. The methodology applied is also more advanced when it comes to data preprocessing steps, making use of state-of-the-art techniques applied to ensure constant performance. With an ever-rising threat from fake media, this piece of research puts great emphasis on stringent memory testing to keep at bay the spread of manipulated content. It also justifies better explanation methods to justify the reasoning done by the model for those decisions that build more trust and reliability. The ensemble models being more accurate have been studied and examined for establishing a possibility of combining various detection frameworks that could together produce superior results. Further, the study underlines the need for real-time detection tools that can be effective on different social media sites and digital environments. Ethics, protecting privacy, and public awareness in the fight against the proliferation of deepfakes are important considerations. By significantly contributing to the advancements made in the technology that has actually advanced detection, it strengthens the safety and integrity of the cyber world with a robust defense against ever-evolving deepfake threats in technology. Overall, the findings generally go a long way to prove themselves as the crucial step forward to ensuring information authenticity and the trustworthiness of society in this digital world.
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    Breast cancer detection using deep learning technique
    (Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü, 2020) Alkurdi, Dunya Ahmed; Ilyas, Muhammad
    Breast cancer has become the most common form of cancer in world recently having overtaken cervical cancer in urban cities. Immense research has been carried out on breast cancer and several automated machines for detection have been formed, however, they are far from perfection and medical assessments need more reliable services. Computer Assisted Diagnostics (CAD) programs have been developed over the past two decades to help radiologists interpret mammogram screening. Deep convolutionary neural networks (CNN), which have surpassed human output since 2012, have been an immense success in image recognition. Deep CNNs will revolutionize the analysis of medical images. We propose a method for breast cancer detection based on Faster R-CNN, The most common frameworks for object detection. In a non-human interference mammogram, the device detects and categorizes malignant or benign lesions. The method proposed sets the current status of the INbreast database public classification scheme, AUC = 0.95. In the digital mammography challenge DREAM with 0.85 = 0.85, the method mentioned here was second. When the device is used as a sensor, the accuracy of the INbreast data set is extremely low with very false positive image points.
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    RETRACTED: Cancer detection using deep learning techniques (Retracted Article)
    (Springer Heidelberg, 2024) Alkurdi, Dunya Ahmed; Ilyas, Muhammad; Jamil, Akhtar
    Breast cancer has become the most common form of cancer in world recently having overtaken cervical cancer in urban cities. Immense research has been carried out on breast cancer and several automated machines for detection have been formed, however, they are far from perfection and medical assessments need more reliable services. Computer Assisted Diagnostics programs have been developed over the past 2 decades to help radiologists interpret mammogram screening. Deep convolutional neural networks (CNN), which have surpassed human output since 2012, have been an immense success in image recognition. Deep CNNs will revolutionize the analysis of medical images. We propose a method for breast cancer detection based on Faster R-CNN, the most common frameworks for object detection. In a non-human interference mammogram, the device detects and categorizes malignant or benign lesions. The method proposed sets the current status of the INbreast database public classification scheme, AUC = 0.95. In the digital mammography challenge DREAM with AUC = 0.85, the method mentioned here was second. When the device is used as a sensor, the accuracy of the INbreast data set is extremely low with very false positive image points.