Deep-learning framework to detect lung abnormality – A study with chest X-Ray and lung CT scan images
| Autor: | João Manuel R. S. Tavares, Suresh Chandra Satapathy, Abhir Bhandary, G. Ananth Prabhu, Charles Shasky, David E. Robbins, Venkatesan Rajinikanth, Yudong Zhang, N. Sri Madhava Raja, K. Palani Thanaraj |
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| Přispěvatelé: | Faculdade de Engenharia |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Feature vector 02 engineering and technology 01 natural sciences Ciências Tecnológicas Ciências médicas e da saúde Artificial Intelligence 0103 physical sciences 0202 electrical engineering electronic engineering information engineering medicine 010306 general physics Lung cancer Lung business.industry Deep learning Ciências médicas e da saúde Cancer Pattern recognition medicine.disease Support vector machine Pneumonia medicine.anatomical_structure Technological sciences Medical and Health sciences Signal Processing Principal component analysis Softmax function Medical and Health sciences 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence Abnormality business Software |
| Zdroj: | Repositório Científico de Acesso Aberto de Portugal Repositório Científico de Acesso Aberto de Portugal (RCAAP) instacron:RCAAP |
| ISSN: | 0167-8655 |
| Popis: | Lung abnormalities are highly risky conditions in humans. The early diagnosis of lung abnormalities is essential to reduce the risk by enabling quick and efficient treatment. This research work aims to propose a Deep-Learning (DL) framework to examine lung pneumonia and cancer. This work proposes two different DL techniques to assess the considered problem: (i) The initial DL method, named a modified AlexNet (MAN), is proposed to classify chest X-Ray images into normal and pneumonia class. In the MAN, the classification is implemented using with Support Vector Machine (SVM), and its performance is compared against Softmax. Further, its performance is validated with other pre-trained DL techniques, such as AlexNet, VGG16, VGG19 and ResNet50. (ii) The second DL work implements a fusion of handcrafted and learned features in the MAN to improve classification accuracy during lung cancer assessment. This work employs serial fusion and Principal Component Analysis (PCA) based features selection to enhance the feature vector. The performance of this DL frame work is tested using benchmark lung cancer CT images of LIDC-IDRI and classification accuracy (97.27%) is attained. |
| Databáze: | OpenAIRE |
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