Deep-learning framework to detect lung abnormality - A study with chest X-Ray and lung CT scan images

• Type: Article in International Scientific Journal
• Date: 2020-01

Authors

Abhir Bhandary (Author) Other; G. Ananth Prabhu (Author) Other; V. Rajinikanth (Author) Other; K. Palani Thanaraj (Author) Other; Suresh Chandra Satapathy (Author) Other; David E. Robbins (Author) Other; Charles Shasky (Author) Other; Yu-Dong Zhang (Author) Other; João Manuel R. S. Tavares (Author) FEUP; N. Sri Madhava Raja (Author) Other

Journal

Title: Pattern Recognition Letters

Vol. 129

Pages: 271-278

ISSN: 0167-8655

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 0 Citations

ISI Web of Science

Scopus - 71 Citations

Scientific classification

CORDIS: Technological sciences

FOS: Medical and Health sciences

Other information

Authenticus ID: P-00R-F4W

DOI: 10.1016/j.patrec.2019.11.013

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8