A portable breast cancer detection system based on smartphone with infrared camera

Ma, Jian; Shang, Pengchao; Chen, Lu; Meraghni, Safa; Benaggoune, Khaled; Zuluaga, Juan; Zerhouni, Noureddine; Devalland, Christine; Masry, Zeina Al

doi:10.21595/vp.2019.20978

Cited by 18 publications

(4 citation statements)

References 15 publications

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“…In supporting early stage breast cancer detection, a portable breast cancer detector has been proposed [134]. However, the NN algorithm applied here has not yet achieved 100% accuracy.…”

Section: ) Mobile Lightweightmentioning

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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“…In supporting early stage breast cancer detection, a portable breast cancer detector has been proposed [134]. However, the NN algorithm applied here has not yet achieved 100% accuracy.…”

Section: ) Mobile Lightweightmentioning

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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“…Recently, few studies attempted the use of mobile phones to create breast cancer early detection systems. The researcher [49] used temperature and texture features to design a breast cancer detection system based on a smartphone with an infrared camera, achieving the accuracy of 99.21 % with the k-Nearest Neighbor classifier. The infrared images with 680×480 resolution were converted to grayscale images and their resolution reduced to 160×120, consistent with the infrared camera of the FLIR Lepton 3.…”

Section: Related Workmentioning

confidence: 99%

A Systematic Review of Breast Cancer Detection Using Thermography and Neural Networks

et al. 2020

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“…The infrared camera can be mounted on various supports to gather thermal images as well. For neighborhood-scale design research, it is mounted on a rooftop observatory [19]; for micro-scale studies, it is used with a tripod, portable device, or smartphone [20]; and for studies spanning the neighborhood scale and building scale, it is mounted on a drone [21]. As a result, several airborne and satellite thermal sensors have been created and are currently being employed directly or indirectly for numerous agricultural applications.…”

Section: Introductionmentioning

confidence: 99%

Classifying the Vermicompost Production Stages Using Thermal Camera Data

Mohamed,

Akl,

Badr

et al. 2023

IEEE Access

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The procedure of processing the vermicompost production includes several stages, where the vermicompost material has different temperatures during these different stages. Thermal sensors play a key role in numerous fields, such as medical and agricultural applications. Thermal cameras can produce a thermal image or an array of values representing the array of sensory data. i.e., an array of temperatures. In this study, we proposed the first thermal imagery dataset of the vermicompost production process. The contributions of this work are two-fold using the proposed dataset. First, we framed the process of predicting the vermicompost production process as a classification problem. Second, we compared classifying the different stages of the process of vermicompost production based on two different input types, namely, thermal images and an array of temperatures. In other words, the classifier will be fed with an input (an image or an array of temperatures), and then the classifier will predict the vermicompost production stage. In this context, we utilized several machine and deep learning models as classifiers. For the utilized dataset, the study has been conducted on a set of images collected during the vermicompost production procedure which was collected every 14 days over 42 consecutive days, i.e., four classes. We proposed running a series of experiments to determine which input type yields better classification accuracy. The obtained results show that using thermal images for the sake of classifying the vermicompost production stages achieved higher accuracy, about 92%, in comparison to using the sensor array data, about 60%.

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A portable breast cancer detection system based on smartphone with infrared camera

Cited by 18 publications

References 15 publications

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

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

A Systematic Review of Breast Cancer Detection Using Thermography and Neural Networks

Classifying the Vermicompost Production Stages Using Thermal Camera Data

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