Identification of interstitial lung diseases using deep learning
| Autor: | Peter Augustine, Nidhin Raju, H. B. Anita |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_specialty
General Computer Science Computer science Computed tomography Convolutional neural network 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Clinical history Medical image analysis Biopsy Medical imaging medicine Medical physics Electrical and Electronic Engineering medicine.diagnostic_test Artificial neural network business.industry Deep learning respiratory system Classification Neural network respiratory tract diseases Identification (information) Artificial intelligence business 030217 neurology & neurosurgery |
| Popis: | The advanced medical imaging provides various advantages to both the patients and the healthcare providers. Medical Imaging truly helps the doctor to determine the inconveniences in a human body and empowers them to make better choices. Deep learning has an important role in the medical field especially for medical image analysis today. It is an advanced technique in the machine learning concept which can be used to get efficient output than using any other previous techniques. In the anticipated work deep learning is used to find the presence of interstitial lung diseases (ILD) by analyzing high-resolution computed tomography (HRCT) images and identifying the ILD category. The efficiency of the diagnosis of ILD through clinical history is less than 20%. Currently, an open chest biopsy is the best way of confirming the presence of ILD. HRCT images can be used effectively to avoid open chest biopsy and improve accuracy. In this proposed work multi-label classification is done for 17 different categories of ILD. The average accuracy of 95% is obtained by extracting features with the help of a convolutional neural network (CNN) architecture called SmallerVGGNet. |
| Databáze: | OpenAIRE |
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