Dimensionality Reduction in Deep Learning for Chest X-Ray Analysis of Lung Cancer

Efficiency of some dimensionality reduction techniques, like lung segmentation, bone shadow exclusion, and t-distributed stochastic neighbor embedding (t-SNE) for exclusion of outliers, is estimated for analysis of chest X-ray (CXR) 2D images by deep learning approach to help radiologists identify marks of lung cancer in CXR. Training and validation of the simple convolutional neural network (CNN) was performed on the open JSRT dataset (dataset #01), the JSRT after bone shadow exclusion - BSE-JSRT (dataset #02), JSRT after lung segmentation (dataset #03), BSE-JSRT after lung segmentation (dataset #04), and segmented BSE-JSRT after exclusion of outliers by t-SNE method (dataset #05). The results demonstrate that the pre-processed dataset obtained after lung segmentation, bone shadow exclusion, and filtering out the outliers by t-SNE (dataset #05) demonstrates the highest training rate and best accuracy in comparison to the other pre-processed datasets.Comment: 6 pages, 14 figure

Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray

Pneumonia is a life-threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life-endangering if not acted upon in the right time and thus an early diagnosis of pneumonia is vital. The aim of this paper is to automatically detect bacterial and viral pneumonia using digital x-ray images. It provides a detailed report on advances made in making accurate detection of pneumonia and then presents the methodology adopted by the authors. Four different pre-trained deep Convolutional Neural Network (CNN)- AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for transfer learning. 5247 Bacterial, viral and normal chest x-rays images underwent preprocessing techniques and the modified images were trained for the transfer learning based classification task. In this work, the authors have reported three schemes of classifications: normal vs pneumonia, bacterial vs viral pneumonia and normal, bacterial and viral pneumonia. The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial and viral pneumonia were 98%, 95%, and 93.3% respectively. This is the highest accuracy in any scheme than the accuracies reported in the literature. Therefore, the proposed study can be useful in faster-diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients.Comment: 13 Figures, 5 tables. arXiv admin note: text overlap with arXiv:2003.1314

Deep Learning Paradigms for Existing and Imminent Lung Diseases Detection: A Review

Diagnosis of lung diseases like asthma, chronic obstructive pulmonary disease, tuberculosis, cancer, etc., by clinicians rely on images taken through various means like X-ray and MRI. Deep Learning (DL) paradigm has magnified growth in the medical image field in current years. With the advancement of DL, lung diseases in medical images can be efficiently identified and classified. For example, DL can detect lung cancer with an accuracy of 99.49% in supervised models and 95.3% in unsupervised models. The deep learning models can extract unattended features that can be effortlessly combined into the DL network architecture for better medical image examination of one or two lung diseases. In this review article, effective techniques are reviewed under the elementary DL models, viz. supervised, semi-supervised, and unsupervised Learning to represent the growth of DL in lung disease detection with lesser human intervention. Recent techniques are added to understand the paradigm shift and future research prospects. All three techniques used Computed Tomography (C.T.) images datasets till 2019, but after the pandemic period, chest radiographs (X-rays) datasets are more commonly used. X-rays help in the economically early detection of lung diseases that will save lives by providing early treatment. Each DL model focuses on identifying a few features of lung diseases. Researchers can explore the DL to automate the detection of more lung diseases through a standard system using datasets of X-ray images. Unsupervised DL has been extended from detection to prediction of lung diseases, which is a critical milestone to seek out the odds of lung sickness before it happens. Researchers can work on more prediction models identifying the severity stages of multiple lung diseases to reduce mortality rates and the associated cost. The review article aims to help researchers explore Deep Learning systems that can efficiently identify and predict lung diseases at enhanced accuracy

Artificial Intelligence Techniques in Medical Imaging: A Systematic Review

This scientific review presents a comprehensive overview of medical imaging modalities and their diverse applications in artificial intelligence (AI)-based disease classification and segmentation. The paper begins by explaining the fundamental concepts of AI, machine learning (ML), and deep learning (DL). It provides a summary of their different types to establish a solid foundation for the subsequent analysis. The prmary focus of this study is to conduct a systematic review of research articles that examine disease classification and segmentation in different anatomical regions using AI methodologies. The analysis includes a thorough examination of the results reported in each article, extracting important insights and identifying emerging trends. Moreover, the paper critically discusses the challenges encountered during these studies, including issues related to data availability and quality, model generalization, and interpretability. The aim is to provide guidance for optimizing technique selection. The analysis highlights the prominence of hybrid approaches, which seamlessly integrate ML and DL techniques, in achieving effective and relevant results across various disease types. The promising potential of these hybrid models opens up new opportunities for future research in the field of medical diagnosis. Additionally, addressing the challenges posed by the limited availability of annotated medical images through the incorporation of medical image synthesis and transfer learning techniques is identified as a crucial focus for future research efforts

Image Features for Tuberculosis Classification in Digital Chest Radiographs

Tuberculosis (TB) is a respiratory disease which affects millions of people each year, accounting for the tenth leading cause of death worldwide, and is especially prevalent in underdeveloped regions where access to adequate medical care may be limited. Analysis of digital chest radiographs (CXRs) is a common and inexpensive method for the diagnosis of TB; however, a trained radiologist is required to interpret the results, and is subject to human error. Computer-Aided Detection (CAD) systems are a promising machine-learning based solution to automate the diagnosis of TB from CXR images. As the dimensionality of a high-resolution CXR image is very large, image features are used to describe the CXR image in a lower dimension while preserving the elements in the CXR necessary for the detection of TB. In this thesis, I present a set of image features using Pyramid Histogram of Oriented Gradients, Local Binary Patterns, and Principal Component Analysis which provides high classifier performance on two publicly available CXR datasets, and compare my results to current state-of-the-art research

The Effectiveness of Transfer Learning Systems on Medical Images

Deep neural networks have revolutionized the performances of many machine learning tasks such as medical image classification and segmentation. Current deep learning (DL) algorithms, specifically convolutional neural networks are increasingly becoming the methodological choice for most medical image analysis. However, training these deep neural networks requires high computational resources and very large amounts of labeled data which is often expensive and laborious. Meanwhile, recent studies have shown the transfer learning (TL) paradigm as an attractive choice in providing promising solutions to challenges of shortage in the availability of labeled medical images. Accordingly, TL enables us to leverage the knowledge learned from related data to solve a new problem. The objective of this dissertation is to examine the effectiveness of TL systems on medical images. First, a comprehensive systematic literature review was performed to provide an up-to-date status of TL systems on medical images. Specifically, we proposed a novel conceptual framework to organize the review. Second, a novel DL network was pretrained on natural images and utilized to evaluate the effectiveness of TL on a very large medical image dataset, specifically Chest X-rays images. Lastly, domain adaptation using an autoencoder was evaluated on the medical image dataset and the results confirmed the effectiveness of TL through fine-tuning strategies. We make several contributions to TL systems on medical image analysis: Firstly, we present a novel survey of TL on medical images and propose a new conceptual framework to organize the findings. Secondly, we propose a novel DL architecture to improve learned representations of medical images while mitigating the problem of vanishing gradients. Additionally, we identified the optimal cut-off layer (OCL) that provided the best model performance. We found that the higher layers in the proposed deep model give a better feature representation of our medical image task. Finally, we analyzed the effect of domain adaptation by fine-tuning an autoencoder on our medical images and provide theoretical contributions on the application of the transductive TL approach. The contributions herein reveal several research gaps to motivate future research and contribute to the body of literature in this active research area of TL systems on medical image analysis