Neural network approach for the determination of heat source parameters from surface temperature image

Hossain, Shazzat; Mohammadi, Fazel; Nejad, E. Talebi

doi:10.1109/ccece.2011.6030634

Cited by 9 publications

(12 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, bio-thermal parameters derived analytically are trivial and meaningless in clinical application. Several contemporary researchers incorporated thermal-electric analogy to find the parameters of a hot nodule [21,57] by mapping the temperature texture over skin of a homogeneous tissue equivalent adiposity phantom. But, due to the interdependency of parameters, unknown shape and thermal behavior of tumor, the solution is murky and cannot be applied for clinical purposes readily.…”

Section: Remarksmentioning

confidence: 99%

“…Mital et al [60] developed an in-vitro mode experimental and evolutionary method, to determined parameters of an embedded heat source representing a tumor using IR. An approximate heat source model of an embedded tumor was developed to determine the correlation among depth, radius and heat generation with skin temperature profile [21,57]. Angelli et al [14] used Finite difference method (FDM) and pattern search algorithm for estimating the depth, size and heat generation of an embedded tumor.…”

Section: Remarksmentioning

confidence: 99%

“…In conclusion, results obtained from those studies were not validated and were not suitable for clinical application. In addition, the Inverse Approach based on the Artificial Neural Network (ANN) [60,57], the Genetic Algorithm (GA) [16], the pattern search algorithms [14] etc. to estimate the physical and thermal parameters of a tumor were not validated with real/clinical data.…”

Section: Remarksmentioning

confidence: 99%

See 2 more Smart Citations

Development of methodology for detection of abnormal functioning region in human external organs using thermogram analysis

Hossain¹

2021

Preprint

View full text Add to dashboard Cite

This study has established thermography as a potential diagnostic tool for detecting and parameterizing tumors even at the earlier stage from abnormal local surface thermal features captured by high sensitive infrared cameras without known risk of morbidity. Discrepant thermal features originate not only for tumor’s distinguishing physio-bio-thermal features but also for the state of health, resulting in thermography as a useful tool for retrieving information about the tissue’s health, thus an efficient screening tool. Accurate linking between hyper functional tissues and thermal pattern could turn the screening tool into a promising parameterizing tool. Human external organs, for example chest, forearm and breast have been modeled, mimicking their shapes, inhomogeneity and deformations to obtain steady-state thermal feature in the tissue interior at healthy state and the computation is extended for tumors buried into healthy tissues for determining abnormal local surface thermal image. Tumor diagnosis parameters have been forecasted from thermogram using an indirect process involving the optimization process. The study has applied gradient (gradient projection method), non-gradient (pattern search method) and learning based (genetic algorithm) optimization approaches. Feasibility of the proposed technique is investigated for tumors in human organs. The local abnormal thermal feature screens earlier stage tumors out and reveal how tumors affect the thermal behaviour and what particular parameters have significant influence on the thermal image. Influential parameters are applied as optimization variables and their influences are also figured out to determine the gradient matrix for the gradient optimization technique. The study has employed bio-heat equations, heat-source model and Artificial Neural Network as governing equation to develop simulated datasets. The simulated dataset is compared with test thermogram to minimize a cost function. In lieu of clinical thermograms, the study has developed pretend thermogram with enveloping the simulated datasets with ±10% random noise. This research has tailored optimization algorithms for estimating tumor depth, size, blood perfusion rate, thermal conductivity, and metabolism and the obtained results show good accuracy. The estimated parameters are given to a trained network to reconstruct the thermal feature, thus, validates the performance of the proposed methodology.

show abstract

Section: Remarksmentioning

confidence: 99%

Section: Remarksmentioning

confidence: 99%

Section: Remarksmentioning

confidence: 99%

See 1 more Smart Citation

Development of methodology for detection of abnormal functioning region in human external organs using thermogram analysis

Hossain¹

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…This may be attributed to late diagnosis, in which case the cancer has manifested or metastasized. On the other hand, when diagnosis is early, the survival index improves [7,8] and can go up to 95% [9]. A number of techniques aimed at early detection of breast cancer have been implemented over the years.…”

Section: Introductionmentioning

confidence: 99%

Wearable infra-red pre-screening smartbra for early detection of breast cancer

2020

View full text Add to dashboard Cite

Breast cancer is the most common cancer in women worldwide, comprising 16% of all female cancers and early diagnosis remains an important detection strategy. The aim of this study was to design and implement a user-friendly SmartBra. An infra-red (IR) imaging sensor was deployed to determine the temperature profile of the breast for this application. The device was tested with approval from the Health Research Ethics Committee of the Lagos University Teaching Hospital using healthy persons and persons already diagnosed with different stages of breast cancer. The results showed high sensitivity and specificity with good intra-examiner reliability for absolute values of mean temperature for the right breast and very good reproducibility for the left breast. Data for healthy participants revealed that the difference in absolute temperature between the left and right breast was less than 1oC, while that of the sick (cancer) participants indicated values greater than 1oC. The device is safe and easy to use and therefore can serve as an adjunct diagnostic device for early detection of breast cancer. Keywords: breast cancer, early detection, infra-red thermography, mammography.

show abstract

“…The objective of this study is proposing a methodology for estimating the depth, size and metabolic rate of an embedded tumor in the lobule region of breast. In order to come up with the goal, a realistic thermal model mimicking the breast geometry and heterogeneity has been developed and heat flow problems are addressed using finite element method (FEM) to generate training vectors for ANN [11]. The train network is then simulated by the predicated parameters which are obtained by optimizing the cost function using the pattern search algorithm (PSM).…”

Section: Introductionmentioning

confidence: 99%

Optimization Method to Estimate Breast Tumour Parameters

Hossain¹,

Mohammadi

2014

Biomedical Engineering / 817: Robotics Applications

View full text Add to dashboard Cite

This paper presents a methodology to predict location, size and hyperactivity level of a breast tumor using temperature profile over the skin surface of the breast that may be captured by infrared thermography or numeric simulation. The estimation methodology includes an evolutionary technique based on artificial neural network (ANN), an optimization scheme based on pattern search algorithm (PSA) with linear constraints and a heat flow analysis on anatomic-accurate (realistic) breast model using finite element method (FEM). Laboratory generated datasets obtained from the FEM are applied to the ANN to associate underlying tumor with surface temperature of the model. The ANN training/testing results are in good agreement with those obtained from numeric method (FEM), thus validates the network performance. The PSA is applied for generation of solution vector sets (tumor parameters) within a given space and the solution sets are employed to produce simulated datasets using the trained ANN. The best solution set is determined by minimizing a cost function involving comparing the target temperature profiles (clinical data) to those obtained by simulation.

show abstract

Neural network approach for the determination of heat source parameters from surface temperature image

Cited by 9 publications

References 11 publications

Development of methodology for detection of abnormal functioning region in human external organs using thermogram analysis

Development of methodology for detection of abnormal functioning region in human external organs using thermogram analysis

Wearable infra-red pre-screening smartbra for early detection of breast cancer

Optimization Method to Estimate Breast Tumour Parameters

Contact Info

Product

Resources

About