Experimenting with Convolutional Neural Network Architectures for the automatic characterization of Solitary Pulmonary Nodules' malignancy rating

  title={Experimenting with Convolutional Neural Network Architectures for the automatic characterization of Solitary Pulmonary Nodules' malignancy rating},
  author={Ioannis D. Apostolopoulos},
Lung Cancer is the most common cause of cancer-related death worldwide. Early and automatic diagnosis of Solitary Pulmonary Nodules (SPN) in Computer Tomography (CT) chest scans can provide early treatment as well as doctor liberation from time-consuming procedures. Deep Learning has been proven as a popular and influential method in many medical imaging diagnosis areas. In this study, we consider the problem of diagnostic classification between benign and malignant lung nodules in CT images… 

A Comprehensive Review of Computer-Aided Diagnosis of Pulmonary Nodules Based on Computed Tomography Scans

A comprehensive review of the application and development of CAD systems is presented, covering public datasets of lung CT scans, commonly used evaluation metrics and various medical competitions, and the advantages of CNNs over traditional image processing methods are summarized.

Hybrid selection framework for class balancing approaches based on integrated CNN and decision making techniques for lung cancer diagnosis

A hybrid framework is proposed between deep learning using the proposed convolutional neural network and multi-criteria decision-making techniques in order to reach an effective and accurate classification model for lung cancer diagnosis and select the best methodology to solve the problem of class imbalance datasets.



DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification

DeepLung has performance comparable to experienced doctors both for the nodule-level and patient-level diagnosis on the LIDC-IDRI dataset and surpassed the performance of experienced doctors based on image modality.

An Appraisal of Lung Nodules Automatic Classification Algorithms for CT Images

It is revealed that lung nodule classification based on deep learning becomes dominant for its excellent performance and the consistency of the research objective and integration of data deserves more attention.

Texture Feature Analysis for Computer-Aided Diagnosis on Pulmonary Nodules

All the observations from this systematic investigation study on the three feature types can lead to the conclusions that the Haralick feature type is a better choice, the use of the full 3D data is beneficial, and an adequate tradeoff between image thickness and noise is desired for an optimal CADx performance.

An appraisal of nodules detection techniques for lung cancer in CT images

Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique

The papers in this special section focus on the technology and applications supported by deep learning, which have proven to be powerful tools for a broad range of computer vision tasks.

The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans.

The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus and is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice.

ImageNet classification with deep convolutional neural networks

A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.

Towards Better Analysis of Deep Convolutional Neural Networks

A hybrid visualization is developed to disclose the multiple facets of each neuron and the interactions between them and a biclustering-based edge bundling method is proposed to reduce visual clutter caused by a large number of connections between neurons.

Diagnostic imaging over the last 50 years: research and development in medical imaging science and technology.

  • K. Doi
  • Medicine
    Physics in medicine and biology
  • 2006
Although some of the modalities are already very sophisticated, further improvements will be made in image quality for MRI, ultrasound and molecular imaging, and the infrastructure of PACS is likely to be improved further in terms of its reliability, speed and capacity.

Visualizing and Understanding Convolutional Networks

A novel visualization technique is introduced that gives insight into the function of intermediate feature layers and the operation of the classifier in large Convolutional Network models, used in a diagnostic role to find model architectures that outperform Krizhevsky et al on the ImageNet classification benchmark.