Automatic Classification and Accuracy by Deep Learning Using CNN Methods in Lung Chest X-Ray Images

Thamilarasi, V.; Roselin, R.

doi:10.1088/1757-899x/1055/1/012099

Cited by 11 publications

(3 citation statements)

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“…The model achieved a SEN of 76.80% and an AUC of 73.20% outperforming six radiologists in the detection of lung cancer. Thamilarasi et al [110] used a DL approach for automatic classification of lung nodules into normal or abnormal. The proposed architecture consists of a custom DCNN model trained and tested on 180 segmented CXR images (90 nodules and 90 non-nodule images) acquired from JSRT dataset.…”

Section: Pulmonary Nodule Detectionmentioning

confidence: 99%

A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography

Nasser

Akhloufi

2023

Diagnostics

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Chest X-ray radiography (CXR) is among the most frequently used medical imaging modalities. It has a preeminent value in the detection of multiple life-threatening diseases. Radiologists can visually inspect CXR images for the presence of diseases. Most thoracic diseases have very similar patterns, which makes diagnosis prone to human error and leads to misdiagnosis. Computer-aided detection (CAD) of lung diseases in CXR images is among the popular topics in medical imaging research. Machine learning (ML) and deep learning (DL) provided techniques to make this task more efficient and faster. Numerous experiments in the diagnosis of various diseases proved the potential of these techniques. In comparison to previous reviews our study describes in detail several publicly available CXR datasets for different diseases. It presents an overview of recent deep learning models using CXR images to detect chest diseases such as VGG, ResNet, DenseNet, Inception, EfficientNet, RetinaNet, and ensemble learning methods that combine multiple models. It summarizes the techniques used for CXR image preprocessing (enhancement, segmentation, bone suppression, and data-augmentation) to improve image quality and address data imbalance issues, as well as the use of DL models to speed-up the diagnosis process. This review also discusses the challenges present in the published literature and highlights the importance of interpretability and explainability to better understand the DL models’ detections. In addition, it outlines a direction for researchers to help develop more effective models for early and automatic detection of chest diseases.

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Section: Pulmonary Nodule Detectionmentioning

confidence: 99%

A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography

Nasser

Akhloufi

2023

Diagnostics

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“…Researchers have harnessed deep learning (DL) techniques to automatically classify lung nodules as either normal or pathological [31]. Through the utilization of the Japanese Society of Radiological Technology (JSRT) dataset's 180 segmented chest X-ray (CXR) images (comprising 90 non-nodule and 90 nodule images), a custom deep convolutional neural network (CNN) model was trained and validated.…”

Section: Pulmonary Nodule Detectionmentioning

confidence: 99%

From Pixels to Pathology: Employing Computer Vision to Decode Chest Diseases in Medical Images

Arslan,

Haider,

Khurshid

et al. 2023

Cureus

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Radiology has been a pioneer in the healthcare industry's digital transformation, incorporating digital imaging systems like picture archiving and communication system (PACS) and teleradiology over the past thirty years. This shift has reshaped radiology services, positioning the field at a crucial junction for potential evolution into an integrated diagnostic service through artificial intelligence and machine learning. These technologies offer advanced tools for radiology's transformation. The radiology community has advanced computer-aided diagnosis (CAD) tools using machine learning techniques, notably deep learning convolutional neural networks (CNNs), for medical image pattern recognition. However, the integration of CAD tools into clinical practice has been hindered by challenges in workflow integration, unclear business models, and limited clinical benefits, despite development dating back to the 1990s. This comprehensive review focuses on detecting chest-related diseases through techniques like chest X-rays (CXRs), magnetic resonance imaging (MRI), nuclear medicine, and computed tomography (CT) scans. It examines the utilization of computer-aided programs by researchers for disease detection, addressing key areas: the role of computer-aided programs in disease detection advancement, recent developments in MRI, CXR, radioactive tracers, and CT scans for chest disease identification, research gaps for more effective development, and the incorporation of machine learning programs into diagnostic tools.

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“…Marques et al [ 177 ] developed a multi-task CNN to classify malignancy nodules with an AUC of 0.783. Thamilarasi et al [ 178 ] proposed an automatic lung nodule classifier based on CNN with an accuracy of 86.67% for the JSRT dataset. Kawathekar et al [ 179 ] developed a lung nodule classifier using a machine-learning technique with an accuracy of 94% and an F1_score of 92% for the LNDb dataset.…”

Section: Lung Cancer Prediction Using Deep Learningmentioning

confidence: 99%

Deep Learning Techniques to Diagnose Lung Cancer

Wang

2022

Cancers

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Medical imaging tools are essential in early-stage lung cancer diagnostics and the monitoring of lung cancer during treatment. Various medical imaging modalities, such as chest X-ray, magnetic resonance imaging, positron emission tomography, computed tomography, and molecular imaging techniques, have been extensively studied for lung cancer detection. These techniques have some limitations, including not classifying cancer images automatically, which is unsuitable for patients with other pathologies. It is urgently necessary to develop a sensitive and accurate approach to the early diagnosis of lung cancer. Deep learning is one of the fastest-growing topics in medical imaging, with rapidly emerging applications spanning medical image-based and textural data modalities. With the help of deep learning-based medical imaging tools, clinicians can detect and classify lung nodules more accurately and quickly. This paper presents the recent development of deep learning-based imaging techniques for early lung cancer detection.

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Automatic Classification and Accuracy by Deep Learning Using CNN Methods in Lung Chest X-Ray Images

Cited by 11 publications

References 1 publication

A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography

A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography

From Pixels to Pathology: Employing Computer Vision to Decode Chest Diseases in Medical Images

Deep Learning Techniques to Diagnose Lung Cancer

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