Wos Kapalı Erişimli Yayınlar

Permanent URI for this collectionhttps://hdl.handle.net/11727/10753

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Subject: search.filters.subject.convolutional neural network

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    Comparative Study for Tuberculosis Detection by Using Deep Learning
    (2021) Karaca, Busra Kubra; Guney, Selda; Dengiz, Berna; Agildere, Muhtesem
    Tuberculosis (TB) is an infectious disease which becomes a significant health problem worldwide. Many people have been affected by this disease owing to deficiency of treatment and late or inaccuracy of diagnosis. Therefore, accurate and early diagnosis is the very major solution to checking and preventing the disease. A chest x-ray is a main diagnostic tool used to diagnose tuberculosis. This diagnostic method is limited by the availability of radiologists and the experience and skills of radiologists in reading x-rays. To overcome such a challenge, a computer-aided diagnosis (CAD) system is supposed for the radiologist to interpret chest x-ray images easily. In this study, a CAD system based upon transfer learning is developed for TB detection using Montgomery Country chest x-ray images. We used the VGG16, VGG19, DenseNet121, MobileNet, and InceptionV3 pre-trained CNN models to extract features automatically and used the Support Vector Machine (SVM) classifier to the detection of tuberculosis. Furthermore, data augmentation techniques were applied to boost the performance results. The proposed method performed the highest accuracy of 98.9% and area under the curve (AUC) of 1.00, respectively, with the DenseNet121 on augmented images.
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    Acoustic Scene Classification Using Spatial Pyramid Pooling With Convolutional Neural Networks
    (2019) Basbug, Ahmet Melih; Sert, Mustafa; 0000-0002-7056-4245; AAB-8673-2019
    Automatic understanding of audio events and acoustic scenes has been an active research topic for researchers from signal processing and machine learning communities. Recognition of acoustic scenes in the real life scenarios is a challenging task due to the diversity of environmental sounds and uncontrolled environments. Efficient methods and feature representations are needed to cope with these challenges. In this study, we address the acoustic scene classification of raw audio signal and propose a cascaded CNN architecture that uses spatial pyramid pooling (SPP, also referred to as spatial pyramid matching) method to aggregate local features coming from convolutional layers of the CNN. We use three well known audio features, namely MFCC, Mel Energy, and spectrogram to represent audio content and evaluate the effectiveness of our proposed CNN-SPP architecture on the DCASE 2018 acoustic scene performance dataset. Our results show that, the proposed CNN-SPP architecture with the spectrogram feature improves the classification accuracy.