Identifying pneumonia in chest X-rays: A deep learning approach

Chest diseases are very serious health problems in the life of people. These diseases include chronic obstructive pulmonary disease, pneumonia, asthma, tuberculosis, and lung diseases. The timely diagnosis of chest diseases is very important. Many methods have been developed for this purpose. In this paper, we demonstrate the feasibility of classifying the chest pathologies in chest X-rays using conventional and deep learning approaches. In the paper, convolutional neural networks (CNNs) are presented for the diagnosis of chest diseases. The architecture of CNN and its design principle are presented. For comparative purpose, backpropagation neural networks (BPNNs) with supervised learning, competitive neural networks (CpNNs) with unsupervised learning are also constructed for diagnosis chest diseases. All the considered networks CNN, BPNN, and CpNN are trained and tested on the same chest X-ray database, and the performance of each network is discussed. Comparative results in terms of accuracy, error rate, and training time between the networks are presented.

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Deep Convolutional Neural Networks for Chest Diseases Detection.

Lung Cancer Detection using CT Scan Images

In this paper we describe a comparative classification of Pneumonia using Convolution Neural Network. The database used was the dataset Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification made available by (Kermany, 2018) with a total of 5863 images, with 2 classes: normal and pneumonia. To evaluate the generalization capacity of the models, cross-validation of k-fold was used. The classification models proved to be efficient compared to the work of (Kermany et al., 2018) which obtained 92.8 % and the present work had an average accuracy of 95.30 %.

Classification of Images of Childhood Pneumonia using Convolutional Neural Networks.

Over decades, computer aided diagnosis (CAD) system has been investigated for detection of lung diseases based on chest X-ray images. Incited by the great success of deep learning, in this work, we propose a novel CAD system to identify bacterial and viral pneumonia in chest radiography. The method consists of two parts, lung regions identification and pneumonia category classification. First, left and right lung regions are segmented and extracted with a fully convolutional networks (FCN) model. The model is trained and tested on the open Japanese society of radiological technology database (JSRT, 241 images) and Montgomery County, Md (MC, 138 images) dataset. After segmentation, a deep convolutional neural network (DCNN) model is used to classify the target lung regions. Then, based on the DCNN model, features of the target lung regions are extracted automatically and the performance is compared with that of manual features. Finally, the DCNN features and manual features are fused together and are put into support vector machines (SVM) classifier for binary classification. The proposed method is evaluated on a dataset of Guangzhou Women and Children's Medical Center, China, with 4,513 pediatric patients in total, aged from 1 to 9 years old, during the period from 2003 to 2017. The performances are measured by different criteria: accuracy, precision, sensitivity, specificity and area under the curve (AUC), which is a comprehensive criterion. The experimental results showed better accuracy (0.8048±0.0202) and sensitivity (0.7755±0.0296) in extracting features by DCNN with transfer learning. The values of AUC varied from 0.6937 to 0.8234. And an ensemble of different kinds of features slightly improved the AUC value from 0.8160±0.0162 to 0.8234±0.0014.

Classification of Bacterial and Viral Childhood Pneumonia Using Deep Learning in Chest Radiography

Chest Radiograph is the preliminary requirement for the identification of lung diseases. Tuberculosis; pneumonia and lung cancer these lung diseases are major health threat. According to recent survey; which was given by WHO; rate of people dying due to late diagnosis of lung diseases is in millions. Early diagnosis of these diseases can curb mortality rate. This paper proposes lung segmentation; lung feature extraction and it's classification using artificial neural network technique for the detection of lung diseases such as TB; lung cancer and pneumonia. We have used the simple image processing techniques like intensity based method and discontinuity based method to detect lung boundaries. Statistical and geometrical features are extracted. Image classification using feed forward and back propagation neural network to detect major lung diseases.

Automatic detection of major lung diseases using Chest Radiographs and classification by feed-forward artificial neural network

Leukemia means blood cancer which is featured by the uncontrolled and abnormal production of white blood cells (leukocytes) by the bone marrow in the blood. Analyzing microscopic blood cell images, diseases can be identified and diagnosed early. Hematologist are using technique of image processing to analyze, detect and identify leukemia types in patients recently. Detection through images is fast and cheap method as there is no special need of equipment for lab testing. In this paper, we proposed a method of detection of leukemia in patients from microscopic white blood cell images. We have focused on the changes in the geometry of cells and statistical parameters like mean and standard deviation which separates white blood cells from other blood components using processing tools like MATLAB and LabVIEW. Images processing steps like image enhancement, image segmentation and feature extraction are applied on microscopic images.

Detection of leukemia in microscopic white blood cell images

Design and Implementation of Stable PID Controller for Interacting Level Control System

This paper presents fast discrete curvelet transform-based anisotropic feature extraction for biomedical image indexing and retrieval. In this work, the curvelet transform is applied on the image and feature vector is calculated using the directional energies of these curvelet coefficients. The effectiveness of the proposed approach has been tested on three well-known databases: Open access series of imaging studies MRI, Emphysema-CT and NEMA-CT. The performance of the proposed system is evaluated using average retrieval precision and average retrieval rate. The experimental results show the superiority of proposed approach over well-known existing methods.

C. Y. Patil

Papers

Automatic detection of major lung diseases using Chest Radiographs and classification by feed-forward artificial neural network

Detection of leukemia in microscopic white blood cell images

Design and Implementation of Stable PID Controller for Interacting Level Control System

Fast discrete curvelet transform-based anisotropic feature extraction for biomedical image indexing and retrieval

Design of Sliding Mode Controller With Proportional Integral Sliding Surface for Robust Regulation and Tracking of Process Control Systems