• Corpus ID: 246652628

Multi-Label Classification of Thoracic Diseases using Dense Convolutional Network on Chest Radiographs

  title={Multi-Label Classification of Thoracic Diseases using Dense Convolutional Network on Chest Radiographs},
  author={Dipkamal Bhusal and Sanjeeb Prasad Panday},
Chest X-ray images are one of the most common medical diagnosis techniques to identify different thoracic diseases. However, identification of pathologies in X-ray images requires skilled manpower and are often cited as a time-consuming task with varied level of interpretation, particularly in cases where the identification of disease only by images is difficult for human eyes. With recent achievements of deep learning in image classification, its application in disease diagnosis has been… 



Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists

CheXNeXt, a convolutional neural network to concurrently detect the presence of 14 different pathologies, including pneumonia, pleural effusion, pulmonary masses, and nodules in frontal-view chest radiographs, achieved radiologist-level performance on 11 pathologies and did not achieve radiologists' level performance on 3 pathologies.

Chest pathology detection using deep learning with non-medical training

This first-of-its-kind experiment shows that Deep learning with ImageNet, a large scale non-medical image database may be a good substitute to domain specific representations, which are yet to be available, for general medical image recognition tasks.

Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks.

Deep learning with DCNNs can accurately classify TB at chest radiography with an AUC of 0.99 and an independent board-certified cardiothoracic radiologist blindly interpreted the images to evaluate a potential radiologist-augmented workflow.

ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases

A new chest X-rays database, namely ChestX-ray8, is presented, which comprises 108,948 frontal-view X-ray images of 32,717 unique patients with the text-mined eight disease image labels from the associated radiological reports using natural language processing, which is validated using the proposed dataset.

CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning

An algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists is developed, and it is found that CheXNet exceeds average radiologist performance on the F1 metric.

Training and Validating a Deep Convolutional Neural Network for Computer-Aided Detection and Classification of Abnormalities on Frontal Chest Radiographs

Current deep CNN architectures can be trained with modest-sized medical data sets to achieve clinically useful performance at detecting and excluding common pathology on chest radiographs.

A survey on deep learning in medical image analysis

Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network

Two deep learning methods are proposed to address three main tasks emerging in the area of skin lesion image processing, i.e., lesion segmentation (task 1), lesion dermoscopic feature extraction (task 2) and lesion classification (task 3).

Deep Learning Approach to Diabetic Retinopathy Detection

An automatic deep-learning-based method for stage detection of diabetic retinopathy by single photography of the human fundus and the multistage approach to transfer learning, which makes use of similar datasets with different labeling are proposed.

Deep features class activation map for thermal face detection and tracking

It is demonstrated that it can be also used for face detection from low resolution thermal images, acquired with a portable camera, and the current state of the art in the area of image classification and face tracking in thermography was significantly outperformed.