Novel Techniques for Classification of Lung Nodules using Deep Learning Approach

Objective: Lung cancer is proving to be one of the deadliest diseases that is haunting mankind in recent years. Timely detection of the lung nodules would surely enhance the survival rate. This paper focusses on the classification of candidate lung nodules into nodules/non-nodules in a CT scan of the patient. A deep learning approach –autoencoder is used for the classification. Investigation/Methodology: Candidate lung nodule patches obtained as the results of the lung segmentation are considered as input to the autoencoder model. The ground truth data from the LIDC repository is prepared and is submitted to the autoencoder training module. After a series of experiments, it is decided to use 4-stacked autoencoder. The model is trained for over 600 LIDC cases and the trained module is tested for remaining data sets. Results: The results of the classification are evaluated with respect to performance measures such as sensitivity, specificity, and accuracy. The results obtained are also compared with other related works and the proposed approach was found to be better by 6.2% with respect to accuracy. Conclusion: In this paper, a deep learning approach –autoencoder has been used for the classification of candidate lung nodules into nodules/non-nodules. The performance of the proposed approach was evaluated with respect to sensitivity, specificity, and accuracy and the obtained values are 82.6%, 91.3%, and 87.0%, respectively. This result is then compared with existing related works and an improvement of 6.2% with respect to accuracy has been observed.

Download Full-text

Deep Learning Based Cardiac MRI Segmentation: Do We Need Experts?

Algorithms ◽

10.3390/a14070212 ◽

2021 ◽

Vol 14 (7) ◽

pp. 212

Author(s):

Youssef Skandarani ◽

Pierre-Marc Jodoin ◽

Alain Lalande

Keyword(s):

Deep Learning ◽

Cardiac Mri ◽

Expert Knowledge ◽

Medical Image Analysis ◽

Ground Truth ◽

Cine Mri ◽

Data Sets ◽

Mri Segmentation ◽

Segmentation Evaluation ◽

Ground Truth Data

Deep learning methods are the de facto solutions to a multitude of medical image analysis tasks. Cardiac MRI segmentation is one such application, which, like many others, requires a large number of annotated data so that a trained network can generalize well. Unfortunately, the process of having a large number of manually curated images by medical experts is both slow and utterly expensive. In this paper, we set out to explore whether expert knowledge is a strict requirement for the creation of annotated data sets on which machine learning can successfully be trained. To do so, we gauged the performance of three segmentation models, namely U-Net, Attention U-Net, and ENet, trained with different loss functions on expert and non-expert ground truth for cardiac cine–MRI segmentation. Evaluation was done with classic segmentation metrics (Dice index and Hausdorff distance) as well as clinical measurements, such as the ventricular ejection fractions and the myocardial mass. The results reveal that generalization performances of a segmentation neural network trained on non-expert ground truth data is, to all practical purposes, as good as that trained on expert ground truth data, particularly when the non-expert receives a decent level of training, highlighting an opportunity for the efficient and cost-effective creation of annotations for cardiac data sets.

Download Full-text

A holistic deep learning approach for identification and classification of sub-solid lung nodules in computed tomographic scans

Computers & Electrical Engineering ◽

10.1016/j.compeleceng.2020.106626 ◽

2020 ◽

Vol 84 ◽

pp. 106626 ◽

Cited By ~ 1

Author(s):

G. Savitha ◽

P. Jidesh

Keyword(s):

Deep Learning ◽

Lung Nodules ◽

Learning Approach ◽

Computed Tomographic

Download Full-text

A Hybrid deep learning model for effective segmentation and classification of lung nodules from CT images

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-212189 ◽

2021 ◽

pp. 1-13

Author(s):

Malathi Murugesan ◽

Kalaiselvi Kaliannan ◽

Shankarlal Balraj ◽

Kokila Singaram ◽

Thenmalar Kaliannan ◽

...

Keyword(s):

Lung Cancer ◽

Deep Learning ◽

Lung Tumor ◽

Median Filter ◽

Lung Nodule ◽

Rapid Identification ◽

Lung Nodules ◽

Classification Models ◽

Deep Learning Model

Deep learning algorithms will be used to detect lung nodule anomalies at an earlier stage. The primary goal of this effort is to properly identify lung cancer, which is critical in preserving a person’s life. Lung cancer has been a source of concern for people all around the world for decades. Several researchers presented numerous issues and solutions for various stages of a computer-aided system for diagnosing lung cancer in its early stages, as well as information about lung cancer. Computer vision is one of the field of artificial intelligence this is a better way to detect and prevent the lung cancer. This study focuses on the stages involved in detecting lung tumor regions, namely pre-processing, segmentation, and classification models. An adaptive median filter is used in pre-processing to identify the noise. The work’s originality seeks to create a simple yet effective model for the rapid identification and U-net architecture based segmentation of lung nodules. This approach focuses on the identification and segmentation of lung cancer by detecting picture normalcy and abnormalities.

Download Full-text

Crowd-sourced data collection to support automatic classification of building footprint data

Proceedings of the ICA ◽

10.5194/ica-proc-1-54-2018 ◽

2018 ◽

Vol 1 ◽

pp. 1-7

Author(s):

Robert Hecht ◽

Matthias Kalla ◽

Tobias Krüger

Keyword(s):

Ground Truth ◽

Data Sets ◽

Building Stock ◽

Ground Truth Data ◽

Additional Information ◽

Reference Information ◽

Building Footprint ◽

Building Characteristics ◽

Different Characteristics

Human settlements are mainly formed by buildings with their different characteristics and usage. Despite the importance of buildings for the economy and society, complete regional or even national figures of the entire building stock and its spatial distribution are still hardly available. Available digital topographic data sets created by National Mapping Agencies or mapped voluntarily through a crowd via Volunteered Geographic Information (VGI) platforms (e.g. OpenStreetMap) contain building footprint information but often lack additional information on building type, usage, age or number of floors. For this reason, predictive modeling is becoming increasingly important in this context. The capabilities of machine learning allow for the prediction of building types and other building characteristics and thus, the efficient classification and description of the entire building stock of cities and regions. However, such data-driven approaches always require a sufficient amount of ground truth (reference) information for training and validation. The collection of reference data is usually cost-intensive and time-consuming. Experiences from other disciplines have shown that crowdsourcing offers the possibility to support the process of obtaining ground truth data. Therefore, this paper presents the results of an experimental study aiming at assessing the accuracy of non-expert annotations on street view images collected from an internet crowd. The findings provide the basis for a future integration of a crowdsourcing component into the process of land use mapping, particularly the automatic building classification.

Download Full-text

WEU-Net: A Weight Excitation U-Net for Lung Nodule Segmentation

10.3233/faia210154 ◽

2021 ◽

Author(s):

Syeda Furruka Banu ◽

Md. Mostafa Kamal Sarker ◽

Mohamed Abdel-Nasser ◽

Hatem A. Rashwan ◽

Domenec Puig

Keyword(s):

Lung Cancer ◽

Deep Learning ◽

Communicable Disease ◽

Lung Nodule ◽

Ct Images ◽

Lung Nodules ◽

Learning Approach ◽

Training Stage ◽

Segmentation Methods ◽

Deep Learning Network

Lung cancer is a dangerous non-communicable disease attacking both women and men and every year it causes thousands of deaths worldwide. Accurate lung nodule segmentation in computed tomography (CT) images can help detect lung cancer early. Since there are different locations and indistinguishable shapes of lung nodules in CT images, the accuracy of the existing automated lung nodule segmentation methods still needs further enhancements. In an attempt towards overcoming the above-mentioned challenges, this paper presents WEU-Net; an end-to-end encoder-decoder deep learning approach to accurately segment lung nodules in CT images. Specifically, we use a U-Net network as a baseline and propose a weight excitation (WE) mechanism to encourage the deep learning network to learn lung nodule-relevant contextual features during the training stage. WEU-Net was trained and validated on a publicly available CT images dataset called LIDC-IDRI. The experimental results demonstrated that WEU-Net achieved a Dice score of 82.83% and a Jaccard similarity coefficient of 70.55%.

Download Full-text