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

By Abhir Bhandary, G. Prabhu, V. Rajinikanth, Palani Thanaraj Krishnan, S. Satapathy, David E. Robbins, Charles Shasky, Yudong Zhang, João Manuel Ribeiro da Silva Tavares, N. M. Raja. Pattern Recognition Letters, 2020

381 citations recorded by the source corpus.

Abstract 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.

Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/ae0ddc554e02e5a56f60512bbd22d8937e93ac7a

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Deep learning approaches for COVID-19 detection based on chest X-ray images (2020, positive): “Bhandary et al. (2020) modified the AlexNet model for the detection of lung abnormalities based on chest X-ray images.”
Using X-ray images and deep learning for automated detection of coronavirus disease (2020, methodology): “The performance of the proposed classification model was evaluated based on accuracy, sensitivity, specificity, precision, and F1 score (Bhandary et al., 2020; Blum & Chawla, 2001).”
Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases (2021, neutral): “The entire code for our multi-classification deep learning models was uploaded to the GitHub website at [55].”