Open Access. Powered by Scholars. Published by Universities.®

Engineering Commons

Open Access. Powered by Scholars. Published by Universities.®

Electrical and Computer Engineering

TÜBİTAK

2022

CNN

Articles 1 - 2 of 2

Full-Text Articles in Engineering

An Efficient End-To-End Deep Neural Network For Interstitial Lung Disease Recognition And Classification, Masum Shah Junayed, Afsana Ahsan Jeny, Md Baharul Islam, Ikhtiar Ahmed, Afm Shahen Shah May 2022

An Efficient End-To-End Deep Neural Network For Interstitial Lung Disease Recognition And Classification, Masum Shah Junayed, Afsana Ahsan Jeny, Md Baharul Islam, Ikhtiar Ahmed, Afm Shahen Shah

Turkish Journal of Electrical Engineering and Computer Sciences

The automated Interstitial Lung Diseases (ILDs) classification technique is essential for assisting clinicians during the diagnosis process. Detecting and classifying ILDs patterns is a challenging problem. This paper introduces an end-to-end deep convolution neural network (CNN) for classifying ILDs patterns. The proposed model comprises four convolutional layers with different kernel sizes and Rectified Linear Unit (ReLU) activation function, followed by batch normalization and max-pooling with a size equal to the final feature map size well as four dense layers. We used the ADAM optimizer to minimize categorical cross-entropy. A dataset consisting of 21328 image patches of 128 CT scans with …


Defect Classification Of Railway Fasteners Using Image Preprocessing And Alightweight Convolutional Neural Network, İlhan Aydin, Mehmet Sevi̇, Mehmet Umut Salur, Erhan Akin Mar 2022

Defect Classification Of Railway Fasteners Using Image Preprocessing And Alightweight Convolutional Neural Network, İlhan Aydin, Mehmet Sevi̇, Mehmet Umut Salur, Erhan Akin

Turkish Journal of Electrical Engineering and Computer Sciences

Railway fasteners are used to securely fix rails to sleeper blocks. Partial wear or complete loss of these components can lead to serious accidents and cause train derailments. To ensure the safety of railway transportation, computer vision and pattern recognition-based methods are increasingly used to inspect railway infrastructure. In particular, it has become an important task to detect defects in railway tracks. This is challenging since rail track images are acquired using a measuring train in varying environmental conditions, at different times of day and in poor lighting conditions, and the resulting images often have low contrast. In this study, …