Designing of Ground-Truth-Annotated DBT-TU-JU Breast Thermogram Database Toward Early Abnormality Prediction

Bhowmik, Mrinal Kanti; Gogoi, Usha Rani; Majumdar, G.; Bhattacharjee, Debotosh; Datta, Dhritiman; Ghosh, Anjan

doi:10.1109/jbhi.2017.2740500

Cited by 40 publications

(11 citation statements)

References 36 publications

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“…RF [20,27]: RF classifier is designed based on combining multiple decision trees to obtain enhanced accuracy of classification. The required parameters for RF with values used for this study are number of tree: 100, quality of each split: Gini, the maximum number of feature for best split: auto, maximum tree depth: None, the minimum number of sample for the split: 20, the minimum size of end node/leaf:1, minimum weight fraction leaf: 0.0, maximum leaf node size: None, minimum impurity decrease: 0.0, bootstrap: True, cross-validation method (oob_score): False, processor number: None, random state: None, verbosity:0, warm_start: False, balanced subsample weight: None.…”

Section: Classificationmentioning

confidence: 99%

Feature Based Automated Detection of COVID-19 from Chest X-Ray Images

Bardhan

Roga

2021

Studies in Systems, Decision and Control

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Nowadays the biggest challenge for health care is controlling the pandemic of Coronavirus disease 2019 . Radiological investigation combining with machine learning can serve as a standardized methodology for detecting COVID-19. Chest X-ray imaging is the most feasible radiological test for COVID-19. Machine learning-based automated classification of COVID-19 from chest X-ray images can act as an assistive method to the medical experts for accurate diagnosis of disease. Aiming at this, the study focused on developing a simplified method of X-ray image based computerized COVID-19 detection through conventional feature extraction and classification approach. The method of X-ray image based COVID-19 detection consists of only two main steps: feature extraction, and classification. In feature extraction, a total of 55 X-ray image texture features is extracted from seven different groups. Classification of COVID-19 has been performed using those extracted features through four different popularly used classifiers. The overall analysis of the study has been performed over two datasets. The Random Forest classifier generates the best accuracy of 98.6% and 98.9% for dataset 1 and 2 with the area under the curve (AUC) values 0.99 and 1 respectively. The outcome of our study provides optimal accuracy of COVID-19 classification using X-ray images compare to existing popular studies in this domain. The reliable and less-complex feature of the proposed method may serve it as a computerized X-ray image based COVID-19 detection mechanism, especially in rural areas where medical experts are not available.

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Section: Classificationmentioning

confidence: 99%

Feature Based Automated Detection of COVID-19 from Chest X-Ray Images

Bardhan

Roga

2021

Studies in Systems, Decision and Control

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“…In the same year, Bhowmik et al [58] also proposed a standard breast thermogram acquisition protocol. Factors influencing thermography such as patient's personal and medical information as well as room condition were adjusted before the examination.…”

Section: B Breast Thermographymentioning

confidence: 99%

A Review on Recent Progress in Thermal Imaging and Deep Learning Approaches for Breast Cancer Detection

et al. 2020

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Developing a breast cancer screening method is very important to facilitate early breast cancer detection and treatment. Building a screening method using medical imaging modality that does not cause body tissue damage (non-invasive) and does not involve physical touch is challenging. Thermography, a non-invasive and non-contact cancer screening method, can detect tumors at an early stage even under precancerous conditions by observing temperature distribution in both breasts. The thermograms obtained on thermography can be interpreted using deep learning models such as convolutional neural networks (CNNs). CNNs can automatically classify breast thermograms into categories such as normal and abnormal. Despite their demostrated utility, CNNs have not been widely used in breast thermogram classification. In this study, we aimed to summarize the current work and progress in breast cancer detection based on thermography and CNNs. We first discuss of breast thermography potential in early breast cancer detection, providing an overview of the availability of breast thermal datasets together with publicly accessible. We also discuss characteristics of breast thermograms and the differences between healthy and cancerous thermographic patterns. Breast thermogram classification using a CNN model is described step by step including a simulation example illustrating feature learning. We cover most research related to the implementation of deep neural networks for breast thermogram classification and propose future research directions for developing representative datasets, feeding the segmented image, assigning a good kernel, and building a lightweight CNN model to improve CNN performance. INDEX TERMS breast cancer; convolutional neural network; deep learning; early detection; thermogram

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“…This method obtained an accuracy of 90.90% using a decision tree classifier. In [35], a ground-truth-annotated breast thermogram database (DBT-TU-JU database) was proposed for the early prediction of breast lesions. The DBT-TU-JU database includes 1100 static thermograms of 100 cases.…”

Section: Related Workmentioning

confidence: 99%

Breast Cancer Detection in Thermal Infrared Images Using Representation Learning and Texture Analysis Methods

2019

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Nowadays, breast cancer is one of the most common cancers diagnosed in women. Mammography is the standard screening imaging technique for the early detection of breast cancer. However, thermal infrared images (thermographies) can be used to reveal lesions in dense breasts. In these images, the temperature of the regions that contain tumors is warmer than the normal tissue. To detect that difference in temperature between normal and cancerous regions, a dynamic thermography procedure uses thermal infrared cameras to generate infrared images at fixed time steps, obtaining a sequence of infrared images. In this paper, we propose a novel method to model the changes on temperatures in normal and abnormal breasts using a representation learning technique called learning-to-rank and texture analysis methods. The proposed method generates a compact representation for the infrared images of each sequence, which is then exploited to differentiate between normal and cancerous cases. Our method produced competitive (AUC = 0.989) results when compared to other studies in the literature.

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Designing of Ground-Truth-Annotated DBT-TU-JU Breast Thermogram Database Toward Early Abnormality Prediction

Cited by 40 publications

References 36 publications

Feature Based Automated Detection of COVID-19 from Chest X-Ray Images

Feature Based Automated Detection of COVID-19 from Chest X-Ray Images

A Review on Recent Progress in Thermal Imaging and Deep Learning Approaches for Breast Cancer Detection

Breast Cancer Detection in Thermal Infrared Images Using Representation Learning and Texture Analysis Methods

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