Yazar "Çakmak, M." seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • Küçük Resim Yok
    Öğe
    Convolutional Neural Network-Based Lung Cancer Nodule Detection Based on Computer Tomography
    (Springer Science and Business Media Deutschland GmbH, 2023) Ahmed, A.H.; Alwan, H.B.; Çakmak, M.
    Because of the great responsiveness of aspiratory knob location, computerized tomography (CT) is generally used to analyze cellular breakdown in the lungs without performing biopsy, which could make actual harm nerves and vessels. Notwithstanding, recognizing threatening and harmless aspiratory knobs stays troublesome. Since CT checks are regularly of low goal, it is challenging for radiologists to peruse the output picture’s subtleties. The proceeded with quick development of CT examine examination frameworks lately has made a squeezing need for cutting edge computational apparatuses to remove helpful highlights to help the radiologist in understanding advancement. PC-supported discovery (CAD) frameworks have been created to diminish notable mistakes by distinguishing the dubious highlights a radiologist searches for in a case survey. Our project aims to compare performance of various low memories, lightweight deep neural net (DNN) architectures for biomedical image analysis. It will involve networks like vanilla 2D CNN, U-Net, 2D SqueezeNet, and 2D MobileNet for two case classifications to discover the existence of lung cancer in patient CT scans of lungs with and without primary phase lung cancer. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
  • Küçük Resim Yok
    Öğe
    Real-Time Vehicle Detection for Surveillance of River Dredging Areas Using Convolutional Neural Networks
    (Modern Education and Computer Science Press, 2023) Al, Bayati, M.A.Z.; Çakmak, M.
    The presence of illegal activities such as illegitimate mining and sand theft in river dredging areas leads to economic losses. However, manual monitoring is expensive and time-consuming. Therefore, automated surveillance systems are preferred to mitigate such activities, as they are accurate and available at all times. In order to monitor river dredging areas, two essential steps for surveillance are vehicle detection and license plate recognition. Most current frameworks for vehicle detection employ plain feed-forward Convolutional Neural Networks (CNNs) as backbone architectures. However, these are scale-sensitive and cannot handle variations in vehicles' scales in consecutive video frames. To address these issues, Scale Invariant Hybrid Convolutional Neural Network (SIH-CNN) architecture is proposed for real-time vehicle detection in this study. The publicly available benchmark UA-DETRAC is used to validate the performance of the proposed architecture. Results show that the proposed SIH-CNN model achieved a mean average precision (mAP) of 77.76% on the UA-DETRAC benchmark, which is 3.94% higher than the baseline detector with real-time performance of 48.4 frames per seconds. © 2023, Modern Education and Computer Science Press. All rights reserved.