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Home / programming language / Online Matlab Projects / Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network
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Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network

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SKU: PROJ7141 Categories: 2017 Projects, Final Year Projects, Digital Image Processing Projects, programming language, Online Matlab Projects Tags: Android Project, Asp.Net Project, C# Project, Computer Engineering Final Year Projects, Computer Science Final Year Projects, Electronics Projects Engineering Students Final Year, Engineering Student Project Ideas, Final Semester Projects, Final Year Project Center, Final Year Projects, ieee ECE Projects, ieee EEE Projects, ieee Final Year Projects, IEEE Projects, ieee Projects Networking, Image Processing Projects, J2EE Project, Java Project, Java Project Code, Matlab Project, PHP Project, Students Projects
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Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network

Abstract— Automated tissue characterization is one of the most crucial components of a computer aided diagnosis(CAD)system For interstitial lung diseases(ILDs). Although much research has been conducted in this field, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied in other domains, such as medical image analysis. In this paper, we propose and evaluate a convolutional neural network (CNN), designed for the classification of ILD patterns. The proposed network consists of 5 convolutional layers with 2 2 kernels and LeakyReLU activations, followed by average pooling with size equal to the size of the final feature maps and three dense layers. The last dense layer has 7 outputs, equivalent to the classes considered: healthy, ground glass opacity (GGO), micronodules, consolidation, reticulation, honeycombing and a combination of GGO/reticulation. To train and evaluate the CNN, we used a dataset of 14696 image patches, derived by 120 CT scans from different scanners and hospitals. To the best of our knowledge, this is the first deep CNN designed for the specific problem. A comparative analysis proved the effectiveness of the proposed CNN against previous methods in a challenging dataset. The classification performance( )demonstrated the potential of CNNs in analyzing lung patterns. < final year projects >

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